Search results for: semantic sentiment analysis

Authors: Nnaemeka Enemchukwu

Abstract:

In modern-day Nigeria, the spirit of Biafra, the defunct secessionist state of former Eastern Nigeria, endures. While some attempt to downplay the historical factors that led to its creation, this paper aims to demonstrate that the 1966 pogroms in Nigeria, which claimed the lives of over 30,000 Igbo people, shattered their faith in the nation's ability to provide security and acceptance. This loss of faith led to a mass exodus from various regions of the country back to their homeland in Eastern Nigeria. Utilizing primary sources such as interviews and archival reports, and secondary sources like books, journals, and websites, this paper will argue that the trauma and terror of the 1966 massacres were the primary drivers of secessionist sentiment and self-determination among the Igbo people, ultimately leading to the declaration of Biafra. By drawing parallels with other historical incidents across the globe, this paper will establish the theoretical connection between shocking events, identity questioning among traumatized groups, and the subsequent rise of nationalistic sentiments seeking to ensure group preservation. To achieve its objective, this paper will employ descriptive, narrative, and chronological methods of analysis to present and discuss its findings.

Keywords: Igbo, pogrom, shock, trauma, nationalism, Biafra

Procedia PDF Downloads 69

Authors: Martin H¨ausl, Maximilian Auch, Johannes Forster, Peter Mandl, Alexander Schill

Abstract:

In order to survive on the market, companies must constantly develop improved and new products. These products are designed to serve the needs of their customers in the best possible way. The creation of new products is also called innovation and is primarily driven by a company’s internal research and development department. However, a new approach has been taking place for some years now, involving external knowledge in the innovation process. This approach is called open innovation and identifies customer knowledge as the most important source in the innovation process. This paper presents a concept of using social media posts as an external source to support the open innovation approach in its initial phase, the Ideation phase. For this purpose, the social media posts are semantically structured with the help of an ontology and the authors are evaluated using graph-theoretical metrics such as density. For the structuring and evaluation of relevant social media posts, we also use the findings of Natural Language Processing, e. g. Named Entity Recognition, specific dictionaries, Triple Tagger and Part-of-Speech-Tagger. The selection and evaluation of the tools used are discussed in this paper. Using our ontology and metrics to structure social media posts enables users to semantically search these posts for new product ideas and thus gain an improved insight into the external sources such as customer needs.

Keywords: idea ontology, innovation management, semantic search, open information extraction

Procedia PDF Downloads 188

Authors: Guoqian Ren, Haijiang Li, Jisong Zhang

Abstract:

Over the past 40 years, urban development has undergone large-scale, high-speed expansion, beyond what was previously considered normal and in a manner not proportionally related to population growth or physical considerations. With more scientific and refined decision-making in the urban construction process, new urbanization approaches, aligned with public-private partnerships (PPPs) which evolved in the early 1990s, have become acceptable and, in some situations, even better solutions to outstanding urban municipal construction projects, especially in developing countries. However, as the main driving force to deal with urban public services, PPPs are still problematic regarding value for money (VFM) process in most large-scale construction projects. This paper therefore reviews recent PPP articles in popular project management journals and relevant toolkits, published in the last 10 years, to identify the indicators that influence VFM within PPPs across regions. With increasing concerns about profitability and environmental and social impacts, the current PPP structure requires a more integrated platform to manage multi-performance project life cycles. Building information modelling (BIM), a popular approach to the procurement process in AEC sectors, provides the potential to ensure VFM while also working in tandem with the semantic approach to holistically measure life cycle costs (LCC) and achieve better sustainability. This paper suggests that BIM applied to the entire PPP life cycle could support holistic decision-making regarding VFM processes and thus meet service targets.

Keywords: public-private partnership, value for money, building information modelling, semantic approach

Procedia PDF Downloads 209

Authors: Amartya Hatua, Trung Nguyen, Andrew Sung

Abstract:

In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter.

Keywords: ARIMA, DTW, information diffusion, LSTM, RNN, time series clustering, time series forecasting, Twitter

Procedia PDF Downloads 391

Authors: Glavan Claudiu

Abstract:

My paper focuses on the cultural semiotic interpretation of the Romanian costume from Banat region, from the perspective of Lotman’s semiotic theory of culture. Using Lotman’s system we will analyse the level of language, text and semiosphere within the unity of Banat’s traditional costume. In order to establish a common language and to communicate, the forms and chromatic compositions were expressed through symbols, which carried semantic meanings with an obvious significant semantic load. The symbols, used in this region, receive a strong specific ethnical mark in its representation, in its compositional and chromatic complexity, in accordance with the values and conceptions of life for the people living here. Thus the signs become a unifying force of this ethnic community. Associated with the signs, were the fabrics used in manufacturing the costumes and the careful selections of colours. For example, softer fabrics like silk associated with red vivid colours were used for young woman sending the message they ready to be married. The unity of these elements created the important message that you were sending to your community. The unity of the symbol, fabrics and choice of colours used on the costume carried out an important message like: marital status, social position, or even the village you belonged to. Using Lotman’s perspective on cultural semiotics we will read and analyse the symbolism of the traditional Romanian art from Banat. We will discover meaning in the codified existence of ancient solar symbols, symbols regarding fertility, religious symbols and very few heraldic symbols. Visual communication makes obvious the importance of semiotic value that the traditional costume is carrying from our ancestors.

Keywords: traditional costume, semiotics, Lotman’s theory of culture, traditional culture, signs and symbols

Procedia PDF Downloads 145

Authors: Travis Morris

Abstract:

The purpose of this study is to measure the intersection of environmental security entities in terrorist propaganda. To the best of author’s knowledge, this is the first study of its kind to examine this intersection within terrorist propaganda. Rosoka, natural language processing software and frame analysis are used to advance our understanding of how environmental frames function as emotive triggers. Violent jihadi demagogues use frames to suggest violent and non-violent solutions to their grievances. Emotive triggers are framed in a way to leverage individual and collective attitudes in psychological warfare. A comparative research design is used because of the differences and similarities that exist between two variants of violent jihadi propaganda that target western audiences. Analysis is based on salience and network text analysis, which generates violent jihadi semantic networks. Findings indicate that environmental frames are used as emotive triggers across both data sets, but also as tactical and information data points. A significant finding is that certain core environmental emotive triggers like “water,” “soil,” and “trees” are significantly salient at the aggregate level across both data sets. All environmental entities can be classified into two categories, symbolic and literal. Importantly, this research illustrates how demagogues use environmental emotive triggers in cyber space from a subcultural perspective to mobilize target audiences to their ideology and praxis. Understanding the anatomy of propaganda construction is necessary in order to generate effective counter narratives in information operations. This research advances an additional method to inform practitioners and policy makers of how environmental security and propaganda intersect.

Keywords: propaganda analysis, emotive triggers environmental security, frames

Procedia PDF Downloads 138

Authors: Rahimah Akbar, Abdullah Al-Hashemi, Hanan Taqi, Taiba Sadeq

Abstract:

As technology continues to develop, teaching curriculums in both schools and universities have begun adopting a more computer/digital based approach to the transmission of knowledge and information, as opposed to the more old-fashioned use of textbooks. This gives rise to the question: Are there any differences in learning from a digital source over learning from a printed source, as in from a textbook? More specifically, which medium of information results in better long-term retention? A review of the confounding factors implicated in understanding the relationship between learning from the two different mediums was done. Alongside this, a 4-week cohort study involving 76 1st year English Language female students was performed, whereby the participants were divided into 2 groups. Group A studied material from a paper source (referred to as the Print Medium), and Group B studied material from a digital source (Digital Medium). The dependent variables were grading of memory recall indexed by a 4 point grading system, and total frequency of item repetition. The study was facilitated by advanced computer software called Super Memo. Results showed that, contrary to prevailing evidence, the Digital Medium group showed no statistically significant differences in terms of the shift from Remember (Episodic) to Know (Semantic) when all confounding factors were accounted for. The shift from Random Guess and Familiar to Remember occurred faster in the Digital Medium than it did in the Print Medium.

Keywords: digital medium, print medium, long-term memory recall, episodic memory, semantic memory, super memo, forgetting index, frequency of repetitions, total time spent

Procedia PDF Downloads 289

Authors: Teklu Urgessa, Joong Seek Lee

Abstract:

Social media is becoming the primary choice of activists to make their voices heard. This fact is coupled by two main reasons. The first reason is the emergence web 2.0, which gave the users opportunity to become content creators than passive recipients. Secondly the control of the mainstream mass media outlets by the governments and individuals with their political and economic interests. This paper aimed at exploring twitter data of network actors talking about the marathon silver medalists on Rio2016, who showed solidarity with the Oromo protesters in Ethiopia on the marathon race finish line when he won silver. The aim is to discover important insight using social network analysis and mining. The hashtag #FeyisaLelisa was used for Twitter network search. The actors’ network was visualized and analyzed. It showed the central influencers during first 10 days in August, were international media outlets while it was changed to individual activist in September. The degree distribution of the network is scale free where the frequency of degrees decay by power low. Text mining was also used to arrive at meaningful themes from tweet corpus about the event selected for analysis. The semantic network indicated important clusters of concepts (15) that provided different insight regarding the why, who, where, how of the situation related to the event. The sentiments of the words in the tweets were also analyzed and indicated that 95% of the opinions in the tweets were either positive or neutral. Overall, the finding showed that Olympic stage protest of the marathoner brought the issue of Oromo protest to the global stage. The new research framework is proposed based for event-based social network analysis and mining based on the practical procedures followed in this research for event-based social media sense making.

Keywords: human rights, Olympics, social media, network analysis, social network ming

Procedia PDF Downloads 257

Authors: Bahareh Golchin, Nooshin Riahi

Abstract:

One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

Keywords: emotion classification, sentiment analysis, social networks, deep neural networks

Procedia PDF Downloads 136

Authors: Elcin Timur Cakmak, Ayse Oguzlar

Abstract:

This study presents the results of the tweets sent by Twitter users on social media about the Russia-Ukraine war by bigram and trigram methods. On February 24, 2022, Russian President Vladimir Putin declared a military operation against Ukraine, and all eyes were turned to this war. Many people living in Russia and Ukraine reacted to this war and protested and also expressed their deep concern about this war as they felt the safety of their families and their futures were at stake. Most people, especially those living in Russia and Ukraine, express their views on the war in different ways. The most popular way to do this is through social media. Many people prefer to convey their feelings using Twitter, one of the most frequently used social media tools. Since the beginning of the war, it is seen that there have been thousands of tweets about the war from many countries of the world on Twitter. These tweets accumulated in data sources are extracted using various codes for analysis through Twitter API and analysed by Python programming language. The aim of the study is to find the word sequences in these tweets by the n-gram method, which is known for its widespread use in computational linguistics and natural language processing. The tweet language used in the study is English. The data set consists of the data obtained from Twitter between February 24, 2022, and April 24, 2022. The tweets obtained from Twitter using the #ukraine, #russia, #war, #putin, #zelensky hashtags together were captured as raw data, and the remaining tweets were included in the analysis stage after they were cleaned through the preprocessing stage. In the data analysis part, the sentiments are found to present what people send as a message about the war on Twitter. Regarding this, negative messages make up the majority of all the tweets as a ratio of %63,6. Furthermore, the most frequently used bigram and trigram word groups are found. Regarding the results, the most frequently used word groups are “he, is”, “I, do”, “I, am” for bigrams. Also, the most frequently used word groups are “I, do, not”, “I, am, not”, “I, can, not” for trigrams. In the machine learning phase, the accuracy of classifications is measured by Classification and Regression Trees (CART) and Naïve Bayes (NB) algorithms. The algorithms are used separately for bigrams and trigrams. We gained the highest accuracy and F-measure values by the NB algorithm and the highest precision and recall values by the CART algorithm for bigrams. On the other hand, the highest values for accuracy, precision, and F-measure values are achieved by the CART algorithm, and the highest value for the recall is gained by NB for trigrams.

Keywords: classification algorithms, machine learning, sentiment analysis, Twitter

Procedia PDF Downloads 73

Authors: Andriana Mkrtchyan, Vahe Khlghatyan

Abstract:

The choice of a profession is one of the most important decisions people make throughout their life. With the development of modern science, technologies, and all the spheres existing in the modern world, more and more professions are being arisen that complicate even more the process of choosing. Hence, there is a need for a guiding platform to help people to choose a profession and the right career path based on their interests, skills, and personality. This review aims at analyzing existing methods of comparing PDF format documents and suggests that a 3-stage approach is implemented for the comparison, that is – 1. text extraction from PDF format documents, 2. comparison of the extracted text via NLP algorithms, 3. comparison representation using special shape and color psychology methodology.

Keywords: color psychology, data acquisition/extraction, data augmentation, disambiguation, natural language processing, outlier detection, semantic similarity, text-mining, user evaluation, visual search

Procedia PDF Downloads 76

Authors: Dora Triki, Irena Vida, Claude Obadia

Abstract:

Social networks such as e-Forums or dating sites often face the reluctance of key members to participate. Relying on the conation theory, this study investigates this phenomenon and proposes solutions to mitigate the issue. We show that highly experienced e-Forum members refuse to share business information in a peer to peer information exchange forums. However, forums managers can mitigate this behavior by developing a sentiment of belongingness to the network. Furthermore, by selecting only elite forum participants with ample experience, they can reduce the reluctance of key information providers to engage in information exchange. Our hypotheses are tested with PLS structural equations modeling using survey data from members of a French e-Forum dedicated to the exchange of business information about exporting.

Keywords: conation, e-Forum, information exchange, members participation

Procedia PDF Downloads 158

Authors: Eleni Maistrou, D. Kosmopoulos, Carolina Moretti, Amalia Konidi, Katerina Boulougoura

Abstract:

InterArch is an ongoing research project that has been running since September 2020. It aims to propose the design of a site-based digital application for archaeological sites and outdoor guided tours, supporting virtual and augmented reality technology. The research project is co‐financed by the European Union and Greek national funds, through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH - CREATE – INNOVATE (project code: Τ2ΕΔΚ-01659). It involves mutual collaboration between academic and cultural institutions and the contribution of an IT applications development company. The research will be completed by July 2023 and will run as a pilot project for the city of Ancient Messene, a place of outstanding natural beauty in the west of Peloponnese, which is considered one of the most important archaeological sites in Greece. The applied research project integrates an interactive approach to the natural environment, aiming at a manifold sensory experience. It combines the physical space of the archaeological site with the digital space of archaeological and cultural data while at the same time, it embraces storytelling processes by engaging an interdisciplinary approach that familiarizes the user with multiple semantic interpretations. The mingling of the real-world environment with its digital and cultural components by using augmented reality techniques could potentially transform the visit on-site into an immersive multimodal sensory experience. To this purpose, an extensive spatial analysis along with a detailed evaluation of the existing digital and non-digital archives is proposed in our project, intending to correlate natural landscape morphology (including archaeological material remains and environmental characteristics) with the extensive historical records and cultural digital data. On-site research was carried out, during which visitors’ itineraries were monitored and tracked throughout the archaeological visit using GPS locators. The results provide our project with useful insight concerning the way visitors engage and interact with their surroundings, depending on the sequence of their itineraries and the duration of stay at each location. InterArch aims to propose the design of a site-based digital application for archaeological sites and outdoor guided tours, supporting virtual and augmented reality technology. Extensive spatial analysis, along with a detailed evaluation of the existing digital and non-digital archives, is used in our project, intending to correlate natural landscape morphology with the extensive historical records and cultural digital data. The results of the on-site research provide our project with useful insight concerning the way visitors engage and interact with their surroundings, depending on the sequence of their itineraries and the duration of stay at each location.

Keywords: archaeological site, digital space, semantic interpretations, cultural heritage

Procedia PDF Downloads 70

Authors: Ahmad Mohammad Al-Harahsheh

Abstract:

Anti-language reflects anti-society; it is a restricted spoken code used among a group of interlocutors because of anti-society. This study aims to shed light on the sociolinguistic characteristics of anti-language used by prisoners in Jordan. The participants included were 15 male-Jordanian prisoners who have recently been released. The data were written, transliterated, and analyzed on the basis of sociolinguistics and discourse analysis. This study draws on sociolinguistic theory of language codes as the theoretical framework. The study concludes that anti-language is a male language and is used for secrecy, as the prisoners' tendency to protect themselves from the police; it is a verbal competition, contest and display. In addition, it is employed to express obnoxious ideas and acts by using more pleasant or blurred words and expressions. Also, the anti-language used by prisoners has six linguistic characteristics in JSA (Jordanian Spoken Arabic), such as relexicalization, neologism, rhyme formation, semantic change, derivation, and metaphorical expressions.

Keywords: anti-language, Jordanian Spoken Arabic, sociolinguistics, prisoners

Procedia PDF Downloads 366

Authors: Montasser Mohamed Abdelwahab Mahmoud, Abdelwahab Saber Esawi

Abstract:

The language of the Qur’an has a wide range of understanding, reflection, and meanings. Therefore, translation of the Qur’an is inevitably nothing but a translation of the interpretation of the meanings of the Qur’an. It requires special competencies and skills for translators so that they can get close to the intended meaning of the verse of the Qur’an and convey it with precision. In the Arabic language, the verbal noun “AlMasdar” is a very important derivative that properly expresses the verbal idea in the form of a noun. It sounds the same as the base form of the verb with minor changes in the vowel pattern. It is one of the important topics in morphology. The morphologists divided verbal nouns into auditory and analogical, and they stated that that the verbal nouns (Masadars) originating from three-letter verbs are auditory, although they set controls for some of them in order to preserve them. As for the lexicographers, they mentioned the verbal nouns while talking about the lexical materials, and in some cases, their explanation of them exceeded that made by the morphologists, especially in their discussion of structures that the morphologists did not refer to in their books. The verb kafara (disbelief), for example, has three patterns, namely: al-kufْr, al-kufrān, and al-kufūr, and it was mentioned in the Holy Qur’an with different connotations. The verb ṣāma (fasted) with his two patterns (al-ṣaūm and al-ṣīām) was mentioned in the Holy Qur’an while their semantic meaning is different. The problem discussed in this research paper lied in the "linguistic loss" committed by translators when dealing with Islamic religious texts, especially the Qur'an. The study tried to identify the strategy adopted by translators of the Holy Qur'an in translating words that were classified as verbal nouns through analyzing the translation rendered by five translations of the Qur’an into English: Yusuf Ali, Pickthall, Mohsin Khan, Muhammad Sarwar, and Shakir. This study was limited to the verbal nouns in the Quraan that originate from three-letter verbs and have different semantic meanings.

Keywords: pattern, three-letter verbs, translation of the Quran, verbal nouns

Procedia PDF Downloads 161

Authors: Zamri Abu Bakar Zamri, Normaly Kamal Ismail Normaly, Mohd Izani Mohamed Rawi Izani

Abstract:

Malay noun compound are phrases that consist of two or more nouns. The key characteristic behind noun compounds lies on its frequent occurrences within the text. Therefore, extracting these noun compounds is essential for several domains of research such as Information Retrieval, Sentiment Analysis and Question Answering. Many research efforts have been proposed in terms of extracting Malay noun compounds using linguistic and statistical approaches. Most of the existing methods have concentrated on the extraction of bi-gram noun+noun compound. However, extracting noun+verb, noun+adjective and noun+prepositional is challenging due to the difficulty of selecting an appropriate method with effective results. Thus, there is still room for improvement in terms of enhancing the effectiveness of compound word extraction. Therefore, this study proposed a combination of linguistic approach and statistical measures in order to enhance the extraction of compound words. Several preprocessing steps are involved including normalization, tokenization, and stemming. The linguistic approach that has been used in this study is Part-of-Speech (POS) tagging. In addition, a new linguistic pattern for named entities has been utilized using a list of Malays named entities in order to enhance the linguistic approach in terms of noun compound recognition. The proposed statistical measures consists of NC-value, NTC-value and NLC value.

Keywords: Compound Word, Noun Compound, Linguistic Approach, Statistical Approach

Procedia PDF Downloads 350

Authors: Oshin Anand, Atanu Rakshit

Abstract:

The vast increment in the e-commerce ventures makes this area a prominent research stream. Besides several quantified parameters, the textual content of reviews is a storehouse of many information that can educate companies and help them earn profit. This study is an attempt in this direction. The article attempts to categorize data based on a computed metric that quantifies the influencing capacity of reviews rendering two categories of high and low influential reviews. Further, each of these document is studied to conclude several product feature categories. Each of these categories along with the computed metric is converted to linguistic identifiers and are used in an association mining model. The article makes a novel attempt to combine feature attraction with quantified metric to categorize review text and finally provide frequent patterns that depict customer preferences. Frequent mentions in a highly influential score depict customer likes or preferred features in the product whereas prominent pattern in low influencing reviews highlights what is not important for customers. This is achieved using a hybrid approach of text mining for feature and term extraction, sentiment analysis, multicriteria decision-making technique and association mining model.

Keywords: association mining, customer preference, frequent pattern, online reviews, text mining

Procedia PDF Downloads 388

Authors: Jaeyoung Lee

Abstract:

Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

Procedia PDF Downloads 129

Authors: Khayriniso Bakhtiyarovna Ganiyeva

Abstract:

The article explores the significance of punctuation in achieving communicative competence. It underscores that effective communication goes beyond simply using punctuation correctly. In the successful completion of a communicative activity, it is important not that the writer correctly uses punctuation marks but that he was able to achieve a goal aimed at expressing a certain meaning. The unanimity of the writer and the reader in the mutual understanding of the text is of primary importance. It should also be taken into account that situational communication provides special informative content and expressiveness of speech. Also, the norms of the situation are determined by the nature of the information in the text, and the punctuation marks expressed in accordance with the norm perform logical-semantic, highlighting expressive-emotional and signaling functions. It is a mistake to classify the signs subject to the norm of the situation as created by the author because they functionally reflect the general stylistic features of different texts. Such signs are among the common signs that are codified only by the semantics and structure of the created text.

Keywords: communicative-pragmatic approach, expressiveness of speech, stylistic features, comparative analysis

Procedia PDF Downloads 55

Authors: Raymond Xu, Cindy Jingru Wang

Abstract:

Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

Procedia PDF Downloads 254

Authors: Jimena Alarcon, Andrea Llorens, Gabriel Hernandez, Maritza Palma, Lucia Navarrete

Abstract:

The research has funding from the Government of Chile and is focused on defining the design of rural primary classroom that stimulates creativity. The relevance of the study consists of its capacity to define adequate educational spaces for the implementation of the design-based learning (DBL) methodology. This methodology promotes creativity and teamwork, generating a meaningful learning experience for students, based on the appreciation of their environment and the generation of projects that contribute positively to their communities; also, is an inquiry-based form of learning that is based on the integration of design thinking and the design process into the classroom. The main goal of the study is to define the design characteristics of rural primary school classrooms, associated with the implementation of the DBL methodology. Along with the change in learning strategies, it is necessary to change the educational spaces in which they develop. The hypothesis indicates that a change in the space and equipment of the classrooms based on the emotions of the students will motivate better learning results based on the implementation of a new methodology. In this case, the pedagogical dynamics require an important interaction between the participants, as well as an environment favorable to creativity. Methodologies from Kansei engineering are used to know the emotional variables associated with their definition. The study is done to 50 students between 6 and 10 years old (average age of seven years), 48% of men and 52% women. Virtual three-dimensional scale models and semantic differential tables are used. To define the semantic differential, self-applied surveys were carried out. Each survey consists of eight separate questions in two groups: question A to find desirable emotions; question B related to emotions. Both questions have a maximum of three alternatives to answer. Data were tabulated with IBM SPSS Statistics version 19. Terms referred to emotions are grouped into twenty concepts with a higher presence in surveys. To select the values obtained as part of the implementation of Semantic Differential, a number expected of 'chi-square test (x2)' frequency calculated for classroom space is considered lower limit. All terms over the N expected a cut point, are included to prepare tables for surveys to find a relation between emotion and space. Statistic contrast (Chi-Square) represents significance level ≥ 0, indicator that frequencies appeared are not random. Then, the most representative terms depend on the variable under study: a) definition of textures and color of vertical surfaces is associated with emotions such as tranquility, attention, concentration, creativity; and, b) distribution of the equipment of the rooms, with emotions associated with happiness, distraction, creativity, freedom. The main findings are linked to the generation of classrooms according to diverse DBL team dynamics. Kansei engineering is the appropriate methodology to know the emotions that students want to feel in the classroom space.

Keywords: creativity, design-based learning, education spaces, emotions

Procedia PDF Downloads 142

Authors: Olga Kononova, Sergey Lyapin

Abstract:

Global information society requirements are transparency and reliability of data, as well as ability to manage information resources independently; particularly to search, to analyze, to evaluate information, thereby obtaining new expertise. Moreover, it is satisfying the society information needs that increases the efficiency of the enterprise management and public administration. The study of structurally organized thematic and semantic contexts of different types, automatically extracted from unstructured data, is one of the important tasks for the application of information technologies in education, science, culture, governance and business. The objectives of this study are the contextual knowledge typologization, selection or creation of effective tools for extracting and analyzing contextual knowledge. Explication of various kinds and forms of the contextual knowledge involves the development and use full-text search information systems. For the implementation purposes, the authors use an e-library 'Humanitariana' services such as the contextual search, different types of queries (paragraph-oriented query, frequency-ranked query), automatic extraction of knowledge from the scientific texts. The multifunctional e-library «Humanitariana» is realized in the Internet-architecture in WWS-configuration (Web-browser / Web-server / SQL-server). Advantage of use 'Humanitariana' is in the possibility of combining the resources of several organizations. Scholars and research groups may work in a local network mode and in distributed IT environments with ability to appeal to resources of any participating organizations servers. Paper discusses some specific cases of the contextual knowledge explication with the use of the e-library services and focuses on possibilities of new types of the contextual knowledge. Experimental research base are science texts about 'e-government' and 'computer games'. An analysis of the subject-themed texts trends allowed to propose the content analysis methodology, that combines a full-text search with automatic construction of 'terminogramma' and expert analysis of the selected contexts. 'Terminogramma' is made out as a table that contains a column with a frequency-ranked list of words (nouns), as well as columns with an indication of the absolute frequency (number) and the relative frequency of occurrence of the word (in %% ppm). The analysis of 'e-government' materials showed, that the state takes a dominant position in the processes of the electronic interaction between the authorities and society in modern Russia. The media credited the main role in these processes to the government, which provided public services through specialized portals. Factor analysis revealed two factors statistically describing the used terms: human interaction (the user) and the state (government, processes organizer); interaction management (public officer, processes performer) and technology (infrastructure). Isolation of these factors will lead to changes in the model of electronic interaction between government and society. In this study, the dominant social problems and the prevalence of different categories of subjects of computer gaming in science papers from 2005 to 2015 were identified. Therefore, there is an evident identification of several types of contextual knowledge: micro context; macro context; dynamic context; thematic collection of queries (interactive contextual knowledge expanding a composition of e-library information resources); multimodal context (functional integration of iconographic and full-text resources through hybrid quasi-semantic algorithm of search). Further studies can be pursued both in terms of expanding the resource base on which they are held, and in terms of the development of appropriate tools.

Keywords: contextual knowledge, contextual search, e-library services, frequency-ranked query, paragraph-oriented query, technologies of the contextual knowledge extraction

Procedia PDF Downloads 359

Authors: Elsa Marta Soares, Ana Rita Valente, Cristiana Rodrigues, Filipa Gonçalves

Abstract:

Background: The assessment of feelings and attitudes of preschool children in relation to stuttering is crucial. Negative experiences can lead to anxiety, worry or frustration. To avoid the worsening of attitudes and feelings related to stuttering, it is important the early detection in order to intervene as soon as possible through an individualized intervention plan. Then it is important to have Portuguese instruments that allow this assessment. Aims: The aim of the present study is to realize the translation and adaptation of the Communication Attitude Test for Children in Preschool Age and Kindergarten (KiddyCat) for EP. Methodology: For the translation and adaptation process, a methodological study was carried out with the following steps: translation, back translation, assessment by a committee of experts and pre-test. This abstract describes the results of the first two phases of this process. The translation was accomplished by two bilingual individuals without experience in health and any knowledge about the instrument. One of them was an English teacher and the other one a Translator. The back-translation was conducted by two Senior Class Teachers that live in United Kingdom without any knowledge in health and about the instrument. Results and Discussion: In translation there were differences in semantic equivalences of various expressions and concepts. A discussion between the two translators, mediated by the researchers, allowed to achieve the consensus version of the translated instrument. Taking into account the original version of KiddyCAT the results demonstrated that back-translation versions were similar to the original version of this assessment instrument. Although the back-translators used different words, they were synonymous, maintaining semantic and idiomatic equivalences of the instrument’s items. Conclusion: This project contributes with an important resource that can be used in the assessment of feelings and attitudes of preschool children who stutter. This was the first phase of the research; expert panel and pretest are being developed. Therefore, it is expected that this instrument contributes to an holistic therapeutic intervention, taking into account the individual characteristics of each child.

Keywords: assessment, feelings and attitudes, preschool children, stuttering

Procedia PDF Downloads 149

Authors: Marc Dietrich

Abstract:

Youth cultures like Punk as much as media relevant to the specific scenes associated with them offer ample opportunity for young people or juvenile adults to construct their personal identities. However, developmental psychology has largely neglected such identity construction processes during the last decades. Such was not always the case: Early developmental psychologists intensely studied youth cultures and their meaningful objects and media in the early 20th century but lost interest when cultural studies and the social sciences occupied the field after World War II. Our project Constructions of Juvenility and Generation(ality), funded by the German Federal Ministry for Education and Research, reintegrates the study of youth cultures and their meaningful objects and media in a developmental psychology perspective. We present an empirical study of the ways in which youth, juvenility, and generation (ality) are constructed and negotiated in underground media like punk fanzines (a portmanteau of fan and magazine), including both semantic and aesthetic aspects of these construction processes within punk culture. The fanzine sample was accessed by the theoretical sampling strategy typical for GTM studies. Acknowledging fanzines as artful self-produced media by scene members for scene members, we conceptualize them as authentic documents of scene norms and values. Drawing on an analysis of both text and (cover) images in Punk fanzines published in Germany (and within a sample dating from 1981 until 2015) using a novel Visual Grounded Theory approach, we found that: a) Juvenility is a highly contested concept in punk culture. Its semantic quality and valuation varies with the perspectives present within the culture (e.g. embryo punks versus older punks); b) Juvenility is constructed as having energy and being socio-critical that does not depend on biological age; c) Juvenility is regarded not an ideal per se in German Punk culture; Punk culture constructs old age in a largely positive way (e.g., as marker of being real and a historical innovator); d) Juvenility is constructed as a habit that should be kept for life as it is constantly adapted to individual biographical trajectories like specific job situations or having a family. Consequently, identity negotiation as documented in the zines attempts to balance subculturally driven perspectives on life and society with the pragmatic requirements of a bourgeois life. The proposed paper will present the main results of this large-scale study of punk fanzines and show how developmental psychology perspectives as represented in the novel methodology applied in it can advance the study of youth cultures.

Keywords: construction of juvenility, developmental psychology, visual GTM, youth culture, fanzines

Procedia PDF Downloads 292

Authors: Monday Agbonica Bello Eje

Abstract:

The language of the print media is characterized by the use of apposition. This linguistic element function strategically in journalistic discourse where it is communicatively necessary to name individuals and provide information about them. Linguistic studies on the language of the print media with bias for apposition have largely dwelt on other areas but the examination of the typology of appositive reference in newspaper discourse. Yet, it is capable of revealing ways writers communicate and provide information necessary for readers to follow and understand the message. The study, therefore, analyses the patterns of appositional occurrences and the typology of reference in newspaper articles. The data were obtained from The Punch and Daily Trust Newspapers. A total of six editions of these newspapers were collected randomly spread over three months. News and feature articles were used in the analysis. Guided by the referential theory of meaning in discourse, the appositions identified were subjected to analysis. The findings show that the semantic relation of coreference and speaker coreference have the highest percentage and frequency of occurrence in the data. This is because the subject matter of news reports and feature articles focuses on humans and the events around them; as a result, readers need to be provided with some form of detail and background information in order to identify as well as follow the discourse. Also, the non-referential relation of absolute synonymy and speaker synonymy no doubt have fewer occurrences and percentages in the analysis. This is tied to a major feature of the language of the media: simplicity. The paper concludes that appositions is mainly used for the purpose of providing the reader with much detail. In this way, the writer transmits information which helps him not only to give detailed yet concise descriptions but also in some way help the reader to follow the discourse.

Keywords: apposition, discourse, newspaper, Nigeria, reference

Procedia PDF Downloads 173

Authors: Mariam Matiashvili

Abstract:

Argumentation is an integral part of our daily communications - formal or informal. Argumentative reasoning, techniques, and language tools are used both in personal conversations and in the business environment. Verbalization of the opinions requires the use of extraordinary syntactic-pragmatic structural quantities - arguments that add credibility to the statement. The study of argumentative structures allows us to identify the linguistic features that make the text argumentative. Knowing what elements make up an argumentative text in a particular language helps the users of that language improve their skills. Also, natural language processing (NLP) has become especially relevant recently. In this context, one of the main emphases is on the computational processing of argumentative texts, which will enable the automatic recognition and analysis of large volumes of textual data. The research deals with the linguistic analysis of the argumentative structures of Georgian political speeches - particularly the linguistic structure, characteristics, and functions of the parts of the argumentative text - claims, support, and attack statements. The research aims to describe the linguistic cues that give the sentence a judgmental/controversial character and helps to identify reasoning parts of the argumentative text. The empirical data comes from the Georgian Political Corpus, particularly TV debates. Consequently, the texts are of a dialogical nature, representing a discussion between two or more people (most often between a journalist and a politician). The research uses the following approaches to identify and analyze the argumentative structures Lexical Classification & Analysis - Identify lexical items that are relevant in argumentative texts creating process - Creating the lexicon of argumentation (presents groups of words gathered from a semantic point of view); Grammatical Analysis and Classification - means grammatical analysis of the words and phrases identified based on the arguing lexicon. Argumentation Schemas - Describe and identify the Argumentation Schemes that are most likely used in Georgian Political Speeches. As a final step, we analyzed the relations between the above mentioned components. For example, If an identified argument scheme is “Argument from Analogy”, identified lexical items semantically express analogy too, and they are most likely adverbs in Georgian. As a result, we created the lexicon with the words that play a significant role in creating Georgian argumentative structures. Linguistic analysis has shown that verbs play a crucial role in creating argumentative structures.

Keywords: georgian, argumentation schemas, argumentation structures, argumentation lexicon

Procedia PDF Downloads 70

Authors: Ebipatei Victoria Tunyan, T. A. Cao, Cheol Young Ock

Abstract:

Detecting subjectively biased statements is a vital task. This is because this kind of bias, when present in the text or other forms of information dissemination media such as news, social media, scientific texts, and encyclopedias, can weaken trust in the information and stir conflicts amongst consumers. Subjective bias detection is also critical for many Natural Language Processing (NLP) tasks like sentiment analysis, opinion identification, and bias neutralization. Having a system that can adequately detect subjectivity in text will boost research in the above-mentioned areas significantly. It can also come in handy for platforms like Wikipedia, where the use of neutral language is of importance. The goal of this work is to identify the subjectively biased language in text on a sentence level. With machine learning, we can solve complex AI problems, making it a good fit for the problem of subjective bias detection. A key step in this approach is to train a classifier based on BERT (Bidirectional Encoder Representations from Transformers) as upstream model. BERT by itself can be used as a classifier; however, in this study, we use BERT as data preprocessor as well as an embedding generator for a Bi-LSTM (Bidirectional Long Short-Term Memory) network incorporated with attention mechanism. This approach produces a deeper and better classifier. We evaluate the effectiveness of our model using the Wiki Neutrality Corpus (WNC), which was compiled from Wikipedia edits that removed various biased instances from sentences as a benchmark dataset, with which we also compare our model to existing approaches. Experimental analysis indicates an improved performance, as our model achieved state-of-the-art accuracy in detecting subjective bias. This study focuses on the English language, but the model can be fine-tuned to accommodate other languages.

Keywords: subjective bias detection, machine learning, BERT–BiLSTM–Attention, text classification, natural language processing

Procedia PDF Downloads 130

Authors: F. Ghandehari, H. Lu, L. El-Jahel, D. Jayasuriya

Abstract:

Disagreement among investors is a fundamental aspect of financial markets, significantly influencing market dynamics. Measuring this disagreement has traditionally posed challenges, often relying on proxies like analyst forecast dispersion, which are limited by biases and infrequent updates. Recent movements in social media indicate that retail investors actively seek financial advice online and can influence the stock market. The evolution of the investing landscape, particularly the rise of social media as a hub for financial advice, provides an alternative avenue for real-time measurement of investor sentiment and disagreement. Platforms like Reddit offer rich, community-driven discussions that reflect genuine investor opinions. This research explores how social media empowers retail investors and the potential of leveraging textual analysis of social media content to capture daily fluctuations in investor disagreement. This study investigates the relationship between daily investor disagreement and trading volume, focusing on the role of social media platforms in shaping market dynamics, specifically using data from WallStreetBets (WSB) on Reddit. This paper uses data from 2020 to 2023 from WSB and analyses 4,896 firms with enough social media activity in WSB to define stock-day level disagreement measures. Consistent with traditional theories that disagreement induces trading volume, the results show significant evidence supporting this claim through different disagreement measures derived from WSB discussions.

Keywords: disagreement, retail investor, social finance, social media

Procedia PDF Downloads 39

Authors: Sara Sahraoui

Abstract:

This research studied the effect of Alzheimer's disease (AD) on language information processing in subjects with Alzheimer’s disease (AD) who were bilingual (French and dialectical Arabic). The results show a disorder of certain semantic aspects of their mother tongue (L1). On the other hand, grammatical levels appeared to be relatively unaffected in oral speech in L1 but were disturbed in the second language (L2). In consequence, we constructed a cognitive-language stimulation protocol for bilingual patients (PSCLAB) to respond to this disorder. The efficacy of this protocol in terms of rehabilitation was assessed in 30 such patients through discourse analysis carried out before and after initiating the protocol. The results show that cognitive/language training using the PSCLAB appears to improve the language behaviour of bilingual patients with AD. However, this survey study aims to verify the satisfaction of patients’ relatives with the results of cognitive language training by PSCLAB. We developed a brief instrument to measure the satisfaction of family members. The results report that the patient's relatives are satisfied with the results of cognitive training by PSCLAB.

Keywords: satisfaction, Alzheimer's disease, rehabilitation, levels language

Procedia PDF Downloads 78

Authors: James Jr. Mashiyane, Risuna Nkolele, Stephanie J. Müller, Gciniwe S. Dlamini, Rebone L. Meraba, Darlington S. Mapiye

Abstract:

When performing language tasks in natural language processing (NLP), the dimensionality of word embeddings is chosen either ad-hoc or is calculated by optimizing the Pairwise Inner Product (PIP) loss. The PIP loss is a metric that measures the dissimilarity between word embeddings, and it is obtained through matrix perturbation theory by utilizing the unitary invariance of word embeddings. Unlike in natural language, in genomics, especially in genome sequence processing, unlike in natural language processing, there is no notion of a “word,” but rather, there are sequence substrings of length k called k-mers. K-mers sizes matter, and they vary depending on the goal of the task at hand. The dimensionality of word embeddings in NLP has been studied using the matrix perturbation theory and the PIP loss. In this paper, the sufficiency and reliability of applying word-embedding algorithms to various genomic sequence datasets are investigated to understand the relationship between the k-mer size and their embedding dimension. This is completed by studying the scaling capability of three embedding algorithms, namely Latent Semantic analysis (LSA), Word2Vec, and Global Vectors (GloVe), with respect to the k-mer size. Utilising the PIP loss as a metric to train embeddings on different datasets, we also show that Word2Vec outperforms LSA and GloVe in accurate computing embeddings as both the k-mer size and vocabulary increase. Finally, the shortcomings of natural language processing embedding algorithms in performing genomic tasks are discussed.

Keywords: word embeddings, k-mer embedding, dimensionality reduction

Procedia PDF Downloads 137