Search results for: multimodal sentiment analysis

Authors: Mahmoud B. Rokaya

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The study of opinion mining and sentiment analysis includes analysis of opinions, sentiments, evaluations, attitudes, and emotions. The rapid growth of social media, social networks, reviews, forum discussions, microblogs, and Twitter, leads to a parallel growth in the field of sentiment analysis. The field of sentiment analysis tries to develop effective tools to make it possible to capture the trends of people. There are two approaches in the field, lexicon-based and corpus-based methods. A lexicon-based method uses a sentiment lexicon which includes sentiment words and phrases with assigned numeric scores. These scores reveal if sentiment phrases are positive or negative, their intensity, and/or their emotional orientations. Creation of manual lexicons is hard. This brings the need for adaptive automated methods for generating a lexicon. The proposed method generates dynamic lexicons based on the corpus and then classifies text using these lexicons. In the proposed method, different approaches are combined to generate lexicons from text. The proposed method classifies the tweets into 5 classes instead of +ve or –ve classes. The sentiment classification problem is written as an optimization problem, finding optimum sentiment lexicons are the goal of the optimization process. The solution was produced based on mathematical programming approaches to find the best lexicon to classify texts. A genetic algorithm was written to find the optimal lexicon. Then, extraction of a meta-level feature was done based on the optimal lexicon. The experiments were conducted on several datasets. Results, in terms of accuracy, recall and F measure, outperformed the state-of-the-art methods proposed in the literature in some of the datasets. A better understanding of the Arabic language and culture of Arab Twitter users and sentiment orientation of words in different contexts can be achieved based on the sentiment lexicons proposed by the algorithm.

Keywords: social media, Twitter sentiment, sentiment analysis, lexicon, genetic algorithm, evolutionary computation

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Authors: Srinaath Anbu Durai, Wang Zhaoxia

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Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon based tools VADER and TextBlob are used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. This paper demonstrates the real world economic applications of sentiment analysis of Twitter data.

Keywords: sentiment analysis, Covid-19, housing price prediction, tweets, social media, Singapore HDB, behavioral economics, neural networks

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Authors: Mohd Ridzwan Yaakub, Muhammad Iqbal Abu Latiffi

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Online business has become popular recently due to the massive amount of information and medium available on the Internet. This has resulted in the huge number of reviews where the consumers share their opinion, criticisms, and satisfaction on the products they have purchased on the websites or the social media such as Facebook and Twitter. However, to analyze customer’s behavior has become very important for organizations to find new market trends and insights. The reviews from the websites or the social media are in structured and unstructured data that need a sentiment analysis approach in analyzing customer’s review. In this article, techniques used in will be defined. Definition of the ontology and description of its possible usage in sentiment analysis will be defined. It will lead to empirical research that related to mobile phones used in research and the ontology used in the experiment. The researcher also will explore the role of preprocessing data and feature selection methodology. As the result, ontology-based approach in sentiment analysis can help in achieving high accuracy for the classification task.

Keywords: feature selection, ontology, opinion, preprocessing data, sentiment analysis

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Authors: Pranav Gunhal

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This paper presents an analysis of sentiment on Twitter towards the Karnataka elections held in 2023, utilizing transformer-based models specifically designed for sentiment analysis in Indic languages. Through an innovative data collection approach involving a combination of novel methods of data augmentation, online data preceding the election was analyzed. The study focuses on sentiment classification, effectively distinguishing between positive, negative, and neutral posts while specifically targeting the sentiment regarding the loss of the Bharatiya Janata Party (BJP) or the win of the Indian National Congress (INC). Leveraging high-performing transformer architectures, specifically IndicBERT, coupled with specifically fine-tuned hyperparameters, the AI models employed in this study achieved remarkable accuracy in predicting the INC’s victory in the election. The findings shed new light on the potential of cutting-edge transformer-based models in capturing and analyzing sentiment dynamics within the Indian political landscape. The implications of this research are far-reaching, providing invaluable insights to political parties for informed decision-making and strategic planning in preparation for the forthcoming 2024 Lok Sabha elections in the nation.

Keywords: sentiment analysis, twitter, Karnataka elections, congress, BJP, transformers, Indic languages, AI, novel architectures, IndicBERT, lok sabha elections

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Authors: Enya Autumn Trenholm-Jensen, Hjalte Hviid Mikkelsen

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This study was done in the pursuit of nuancing the discussion surrounding what it means to be human in a time of unparalleled technological development. Subjectivity was deemed to be an accessible example of humanity to study, and art was a fitting medium through which to probe subjectivity. Upon careful theoretical consideration, abstract art was found to fit the parameters of the study with the added bonus of being, as of yet, uninterpretable from an AI perspective. It was hypothesised that dissimilar appraisals of the art stimuli would be found through sentiment and terminology. Opinion data was collected through survey responses and analysed using Valence Aware Dictionary for sEntiment Reasoning (VADER) sentiment analysis. The results reflected the enigmatic nature of subjectivity through erratic ratings of the art stimuli. However, significant themes were found in the terminology used in the responses. The implications of the findings are discussed in relation to the uniqueness, or lack thereof, of human subjectivity, and directions for future research are provided.

Keywords: abstract art, artificial intelligence, cognition, sentiment, subjectivity

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Authors: Josiel Delgadillo, Johnson Kinyua, Charles Mutigwe

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The purpose of sentiment analysis is to determine the sentiment strength (e.g., positive, negative, neutral) from a textual source for good decision-making. Natural language processing in domains such as financial markets requires knowledge of domain ontology, and pre-trained language models, such as BERT, have made significant breakthroughs in various NLP tasks by training on large-scale un-labeled generic corpora such as Wikipedia. However, sentiment analysis is a strong domain-dependent task. The rapid growth of social media has given users a platform to share their experiences and views about products, services, and processes, including financial markets. StockTwits and Twitter are social networks that allow the public to express their sentiments in real time. Hence, leveraging the success of unsupervised pre-training and a large amount of financial text available on social media platforms could potentially benefit a wide range of financial applications. This work is focused on sentiment analysis using social media text on platforms such as StockTwits and Twitter. To meet this need, SkyBERT, a domain-specific language model pre-trained and fine-tuned on financial corpora, has been developed. The results show that SkyBERT outperforms current state-of-the-art models in financial sentiment analysis. Extensive experimental results demonstrate the effectiveness and robustness of SkyBERT.

Keywords: BERT, financial markets, Twitter, sentiment analysis

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Authors: Xu Jiaqiao

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Text sentiment analysis is an important branch of natural language processing. This technology is widely used in public opinion analysis and web surfing recommendations. At present, the mainstream sentiment analysis methods include three parts: sentiment analysis based on a sentiment dictionary, based on traditional machine learning, and based on deep learning. This paper mainly analyzes and compares the advantages and disadvantages of the SVM method of traditional machine learning and the Long Short-term Memory (LSTM) method of deep learning in the field of Chinese sentiment analysis, using Chinese comments on Sina Microblog as the data set. Firstly, this paper classifies and adds labels to the original comment dataset obtained by the web crawler, and then uses Jieba word segmentation to classify the original dataset and remove stop words. After that, this paper extracts text feature vectors and builds document word vectors to facilitate the training of the model. Finally, SVM and LSTM models are trained respectively. After accuracy calculation, it can be obtained that the accuracy of the LSTM model is 85.80%, while the accuracy of SVM is 91.07%. But at the same time, LSTM operation only needs 2.57 seconds, SVM model needs 6.06 seconds. Therefore, this paper concludes that: compared with the SVM model, the LSTM model is worse in accuracy but faster in processing speed.

Keywords: sentiment analysis, support vector machine, long short-term memory, Chinese microblog comments

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Authors: Sarif Ullah Patwary, Matthew Heinrich, Brandon Payne

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The ubiquity of web 2.0 platforms, in the form of wikis, social media (e.g., Facebook, Twitter, etc.) and online review portals (e.g., Yelp), helps shape today’s apparel consumers’ purchasing decision. Online reviews play important role towards consumers’ apparel purchase decision. Each of the consumer reviews carries a sentiment (positive, negative or neutral) towards products. Commercially, apparel brands and retailers analyze sentiment of this massive amount of consumer review data to update their inventory and bring new products in the market. The purpose of this study is to analyze consumer reviews of selected apparel stores with a view to understand, 1) the difference of sentiment expressed through men’s and woman’s text reviews, 2) the difference of sentiment expressed through men’s and woman’s star-based reviews, and 3) the difference of sentiment between star-based reviews and text-based reviews. A total of 9,363 reviews (1,713 men and 7,650 women) were collected using Yelp Dataset Challenge. Sentiment analysis of collected reviews was carried out in two dimensions: star-based reviews and text-based reviews. Sentiment towards apparel stores expressed through star-based reviews was deemed: 1) positive for 3 or 4 stars 2) negative for 1 or 2 stars and 3) neutral for 3 stars. Sentiment analysis of text-based reviews was carried out using Bing Liu dictionary. The analysis was conducted in IPyhton 5.0. Space. The sentiment analysis results revealed the percentage of positive text reviews by men (80%) and women (80%) were identical. Women reviewers (12%) provided more neutral (e.g., 3 out of 5 stars) star reviews than men (6%). Star-based reviews were more negative than the text-based reviews. In other words, while 80% men and women wrote positive reviews for the stores, less than 70% ended up giving 4 or 5 stars in those reviews. One of the key takeaways of the study is that star reviews provide slightly negative sentiment of the consumer reviews. Therefore, in order to understand sentiment towards apparel products, one might need to combine both star and text aspects of consumer reviews. This study used a specific dataset consisting of selected apparel stores from particular geographical locations (the information was not given for privacy concern). Future studies need to include more data from more stores and locations to generalize the findings of the study.

Keywords: apparel, consumer review, sentiment analysis, gender

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Authors: Vo Ngoc Phu, Phan Thi Tuoi

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We have explored different methods of improving the accuracy of sentiment classification. The sentiment orientation of a document can be positive (+), negative (-), or neutral (0). We combine five dictionaries from [2, 3, 4, 5, 6] into the new one with 21137 entries. The new dictionary has many verbs, adverbs, phrases and idioms, that are not in five ones before. The paper shows that our proposed method based on the combination of Term-Counting method and Enhanced Contextual Valence Shifters method has improved the accuracy of sentiment classification. The combined method has accuracy 68.984% on the testing dataset, and 69.224% on the training dataset. All of these methods are implemented to classify the reviews based on our new dictionary and the Internet Movie data set.

Keywords: sentiment classification, sentiment orientation, valence shifters, contextual, valence shifters, term counting

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Authors: LuoJiaoyang, Yu Hongyang

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In this paper, we experimented with a new approach to multimodal identification using RGB, RGB-D and voice data. The multimodal combination of RGB and voice data has been applied in tasks such as emotion recognition and has shown good results and stability, and it is also the same in identity recognition tasks. We believe that the data of different modalities can enhance the effect of the model through mutual reinforcement. We try to increase the three modalities on the basis of the dual modalities and try to improve the effectiveness of the network by increasing the number of modalities. We also implemented the single-modal identification system separately, tested the data of these different modalities under clean and noisy conditions, and compared the performance with the multimodal model. In the process of designing the multimodal model, we tried a variety of different fusion strategies and finally chose the fusion method with the best performance. The experimental results show that the performance of the multimodal system is better than that of the single modality, especially in dealing with noise, and the multimodal system can achieve an average improvement of 5%.

Keywords: multimodal, three modalities, RGB-D, identity verification

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Authors: Lucas A. Silva, Carla R. Aguiar

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This work presents a proposal to perform contextual sentiment analysis using a supervised learning algorithm and disregarding the extensive training of annotators. To achieve this goal, a web platform was developed to perform the entire procedure outlined in this paper. The main contribution of the pipeline described in this article is to simplify and automate the annotation process through a system of analysis of congruence between the notes. This ensured satisfactory results even without using specialized annotators in the context of the research, avoiding the generation of biased training data for the classifiers. For this, a case study was conducted in a blog of entrepreneurship. The experimental results were consistent with the literature related annotation using formalized process with experts.

Keywords: sentiment analysis, untrained annotators, naive bayes, entrepreneurship, contextualized classifier

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Authors: Danielle Shackley, Yetunde Folajimi

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As more people turn to the internet seeking health-related information, there is more risk of finding false, inaccurate, or dangerous information. Sentiment analysis is a natural language processing technique that assigns polarity scores to text, ranging from positive, neutral, and negative. In this research, we evaluate the weight of a sentiment analysis feature added to fake health news classification models. The dataset consists of existing reliably labeled health article headlines that were supplemented with health information collected about COVID-19 from social media sources. We started with data preprocessing and tested out various vectorization methods such as Count and TFIDF vectorization. We implemented 3 Naive Bayes classifier models, including Bernoulli, Multinomial, and Complement. To test the weight of the sentiment analysis feature on the dataset, we created benchmark Naive Bayes classification models without sentiment analysis, and those same models were reproduced, and the feature was added. We evaluated using the precision and accuracy scores. The Bernoulli initial model performed with 90% precision and 75.2% accuracy, while the model supplemented with sentiment labels performed with 90.4% precision and stayed constant at 75.2% accuracy. Our results show that the addition of sentiment analysis did not improve model precision by a wide margin; while there was no evidence of improvement in accuracy, we had a 1.9% improvement margin of the precision score with the Complement model. Future expansion of this work could include replicating the experiment process and substituting the Naive Bayes for a deep learning neural network model.

Keywords: sentiment analysis, Naive Bayes model, natural language processing, topic analysis, fake health news classification model

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Authors: Anas Alsuhaibani

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Observing debate and discussion over social media has been found to be a promising tool to investigate different types of opinion. On 23 June 2016, Brexit voters in the UK decided to depart from the EU, with 51.9% voting to leave. On Twitter, there had been a massive debate in this context, and the hashtag Brexit was allocated as number six of the most tweeted hashtags across the globe in 2016. The study aimed to investigate the sentiment and themes expressed in a sample of tweets during a political event (Brexit) in 2016. A sentiment and thematic analysis was conducted on 1304 randomly selected tweets tagged with the hashtag Brexit in Twitter for the period from 10 June 2016 to 7 July 2016. The data were coded manually into two code frames, sentiment and thematic, and the reliability of coding was assessed for both codes. The sentiment analysis of the selected sample found that 45.63% of tweets conveyed negative emotions while there were only 10.43% conveyed positive emotions. It also surprisingly resulted that 29.37% were factual tweets, where the tweeter expressed no sentiment and the tweet conveyed a fact. For the thematic analysis, the economic theme dominated by 23.41%, and almost half of its discussion was related to business within the UK and the UK and global stock markets. The study reported that the current UK government and relation to campaign themes were the most negative themes. Both sentiment and thematic analyses found that tweets with more than one opinion or theme were rare, 8.29% and 6.13%, respectively.

Keywords: Brexit, political opinion mining, social media, twitter

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Authors: Jiajun Wang, Xiaoge Li

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The purpose of the aspect-level sentiment analysis task is to identify the sentiment polarity of aspects in a sentence. Currently, most methods mainly focus on using neural networks and attention mechanisms to model the relationship between aspects and context, but they ignore the dependence of words in different ranges in the sentence, resulting in deviation when assigning relationship weight to other words other than aspect words. To solve these problems, we propose a new aspect-level sentiment analysis model that combines a multi-channel convolutional network and graph convolutional network (GCN). Firstly, the context and the degree of association between words are characterized by Long Short-Term Memory (LSTM) and self-attention mechanism. Besides, a multi-channel convolutional network is used to extract the features of words in different ranges. Finally, a convolutional graph network is used to associate the node information of the dependency tree structure. We conduct experiments on four benchmark datasets. The experimental results are compared with those of other models, which shows that our model is better and more effective.

Keywords: aspect-level sentiment analysis, attention, multi-channel convolution network, graph convolution network, dependency tree

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Authors: Xing Lyu

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Multimodal metaphor promotes the further development and refinement of multimodal discourse study. Cultural aspects matter a lot not only in creating but also in comprehending multimodal metaphor. By analyzing the target domain and the source domain in 10 public service advertisements of China and Germany about environmental protection, this paper compares the source when the target is alike in each multimodal metaphor in order to seek similarities and differences across cultures. The findings are as follows: first, the multimodal metaphors center around three major topics: the earth crisis, consequences of environmental damage, and appeal for environmental protection; second, the multimodal metaphors mainly grounded in three universal conceptual metaphors which focused on high level is up; earth is mother and all lives are precious. However, there are five Chinese culture-specific multimodal metaphors which are not discovered in Germany ads: east is high leve; a purposeful life is a journey; a nation is a person; good is clean, and water is mother. Since metaphors are excellent instruments on studying ideology, this study can be helpful on intercultural/cross-cultural communication.

Keywords: multimodal metaphor, cultural aspects, public service advertising, cross-cultural communication

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Authors: Yu Hung Chiang, Hei Chia Wang

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Nowadays, tourism has become a part of life. Before reserving hotels, customers need some information, which the most important source is online reviews, about hotels to help them make decisions. Due to the dramatic growing of online reviews, it is impossible for tourists to read all reviews manually. Therefore, designing an automatic review analysis system, which summarizes reviews, is necessary for them. The main purpose of the system is to understand the opinion of reviews, which may be positive or negative. In other words, the system would analyze whether the customers who visited the hotel like it or not. Using sentiment analysis methods will help the system achieve the purpose. In sentiment analysis methods, the targets of opinion (here they are called the feature) should be recognized to clarify the polarity of the opinion because polarity of the opinion may be ambiguous. Hence, the study proposes an unsupervised method using Part-Of-Speech pattern and multi-lexicons sentiment analysis to summarize all reviews. We expect this method can help customers search what they want information as well as make decisions efficiently.

Keywords: text mining, sentiment analysis, product feature extraction, multi-lexicons

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Authors: Sungzoon Cho, Hye Jin Lee, Sungwhan Jeon, Dongyoung Min, Sungwon Lyu

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Major public corporations worldwide use conference calls to report their quarterly earnings. These 'earnings' conference calls allow for questions from stock analysts. We investigated if it is possible to identify sentiment from the call script and use it to predict stock price movement. We analyzed call scripts from six companies, two each from Korea, China and Indonesia during six years 2011Q1 – 2017Q2. Random forest with Frequency-based sentiment scores using Loughran MacDonald Dictionary did better than control model with only financial indicators. When the stock prices went up 20 days from earnings release, our model predicted correctly 77% of time. When the model predicted 'up,' actual stock prices went up 65% of time. This preliminary result encourages us to investigate advanced sentiment scoring methodologies such as topic modeling, auto-encoder, and word2vec variants.

Keywords: earnings call script, random forest, sentiment analysis, stock price prediction

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Authors: William Feng, Qingyu Gao

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Despite the extensive discussions about Hong Kong’s sentiments towards the Chinese Mainland since the sovereignty transfer in 1997, there has been no large-scale empirical analysis of the changing attitudes in the mainstream media, which both reflect and shape sentiments in the society. To address this gap, the present study uses an optimised semantic-based automatic sentiment analysis method to examine a corpus of news about China from 1997 to 2020 in three main Chinese-language newspapers in Hong Kong, namely Apple Daily, Ming Pao, and Oriental Daily News. The analysis shows that although the Hong Kong press had a positive emotional tone toward China in general, the overall trend of sentiment was becoming increasingly negative. Meanwhile, the alignment and antagonism toward China have both increased, providing empirical evidence of attitudinal polarisation in the Hong Kong society. Specifically, Apple Daily’s depictions of China have become increasingly negative, though with some positive turns before 2008, whilst Oriental Daily News has consistently expressed more favourable sentiments. Ming Pao maintained an impartial stance toward China through an increased but balanced representation of positive and negative sentiments, with its subjectivity and sentiment intensity growing to an industry-standard level. The results provide new insights into the complexity of sentiments towards China in the Hong Kong press and media attitudes in general in terms of the “us” and “them” positioning by explicating the cross-newspaper and cross-period variations using an enhanced sentiment analysis method which incorporates sentiment-oriented and semantic role analysis techniques.

Keywords: media attitude, sentiment analysis, Hong Kong press, one country two systems

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Authors: Victoria L. Malakhova

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The research is devoted to multimodal content and its effectiveness in developing students’ linguistic and intercultural communicative competences as an indefeasible constituent of their future professional activity. Description of multimodal content both as a linguistic and didactic phenomenon makes the study relevant. The objective of the article is the analysis of creolized texts and the effect they have on fostering higher education students’ skills and their productivity. The main methods used are linguistic text analysis, qualitative and quantitative methods, deduction, generalization. The author studies texts with full and partial creolization, their features and role in composing multimodal textual space. The main verbal and non-verbal markers and paralinguistic means that enhance the linguo-pragmatic potential of creolized texts are covered. To reveal the efficiency of multimodal content application in English teaching, the author conducts an experiment among both undergraduate students and teachers. This allows specifying main functions of creolized texts in the process of language learning, detecting ways of enhancing students’ competences, and increasing their motivation. The described stages of using creolized texts can serve as an algorithm for work with multimodal content in teaching English as a foreign language. The findings contribute to improving the efficiency of the academic process.

Keywords: creolized text, English language learning, higher education, language and communication competences, multimodal content

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Authors: Smah Almotiri

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One of the most common fields of natural language processing (NLP) is sentimental analysis. The inferred feeling in the text can be successfully mined for various events using sentiment analysis. Twitter is viewed as a reliable data point for sentimental analytics studies since people are using social media to receive and exchange different types of data on a broad scale during the COVID-19 epidemic. The processing of such data may aid in making critical decisions on how to keep the situation under control. The aim of this research is to look at how sentimental states differed in a single geographic region during the lockdown at two different times.1162 tweets were analyzed related to the COVID-19 pandemic lockdown using keywords hashtags (lockdown, COVID-19) for the first sample tweets were from March 23, 2020, until April 23, 2020, and the second sample for the following year was from March 1, 2020, until April 4, 2020. Natural language processing (NLP), which is a form of Artificial intelligence, was used for this research to calculate the sentiment value of all of the tweets by using AFINN Lexicon sentiment analysis method. The findings revealed that the sentimental condition in both different times during the region's lockdown was positive in the samples of this study, which are unique to the specific geographical area of New Zealand. This research suggests applying machine learning sentimental methods such as Crystal Feel and extending the size of the sample tweet by using multiple tweets over a longer period of time.

Keywords: sentiment analysis, Twitter analysis, lockdown, Covid-19, AFINN, NodeJS

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Authors: Félix David Suárez Bonilla

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A sentiment analysis service to detect polarity (positive, neural, and negative), based on transfer learning, was built using a Spanish version of BERT and applied to tweets written in Spanish. The dataset that was used consisted of 11975 reviews, which were extracted from Google Play using the google-play-scrapper package. The BETO trained model used: the AdamW optimizer, a batch size of 16, a learning rate of 2x10⁻⁵ and 10 epochs. The system was tested using tweets of three presidential pre-candidates from Costa Rica. The system was finally validated using human labeled examples, achieving an accuracy of 83.3%.

Keywords: NLP, transfer learning, BERT, sentiment analysis, social media, opinion mining

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Authors: Shatha Khuzaee

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The study develops multimodal stylistic approach to analyse a number of BBC online news articles reporting some key events from the so called ‘Arab Uprisings’. Critical stylistics (CS) and visual grammar (VG) provide insightful arguments to the ways ideology is projected through different verbal and visual modes, yet they are mode specific because they examine how each mode projects its meaning separately and do not attempt to clarify what happens intersemiotically when the two modes co-occur. Therefore, it is the task undertaken in this research to propose multimodal stylistic approach that addresses the issue of ideology construction when the two modes co-occur. Informed by functional grammar and social semiotics, the analysis attempts to integrate three linguistic models developed in critical stylistics, namely, transitivity choices, prioritizing and hypothesizing along with their visual equivalents adopted from visual grammar to investigate the way ideology is constructed, in multimodal text, when text/image participate and interrelate in the process of meaning making on the textual level of analysis. The analysis provides comprehensive theoretical and analytical elaborations on the different points of integration between CS linguistic models and VG equivalents which operate on the textual level of analysis to better account for ideology construction in news as non-literary multimodal texts. It is argued that the analysis well thought out a plan that would remark the first step towards the integration between the well-established linguistic models of critical stylistics and that of visual analysis to analyse multimodal texts on the textual level. Both approaches are compatible to produce multimodal stylistic approach because they intend to analyse text and image depending on whatever textual evidence is available. This supports the analysis maintain the rigor and replicability needed for a stylistic analysis like the one undertaken in this study.

Keywords: multimodality, stylistics, visual grammar, social semiotics, functional grammar

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Authors: Andrey V. Timofeev, Dmitry V. Egorov

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This paper introduces an original method of parametric optimization of the structure for multimodal decision-level fusion scheme which combines the results of the partial solution of the classification task obtained from assembly of the mono-modal classifiers. As a result, a multimodal fusion classifier which has the minimum value of the total error rate has been obtained.

Keywords: classification accuracy, fusion solution, total error rate, multimodal fusion classifier

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Authors: Pilar Rey del Castillo

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This document describes the building of an average indicator of the general sentiments about the future exposed in the newspapers in Spain. The raw data are collected through the scraping of the Digital Periodical and Newspaper Library website. Basic tools of natural language processing are later applied to the collected information to evaluate the sentiment strength of each word in the texts using a polarized dictionary. The last step consists of summarizing these sentiments to produce daily indices. The results are a first insight into the applicability of these techniques to produce periodic sentiment indicators.

Keywords: natural language processing, periodic indicator, sentiment analysis, web scraping

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Authors: Swarnadip Ghosh

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Sentiment Analysis is the process of detecting the contextual polarity of text. In other words, it determines whether a piece of writing is positive, negative or neutral.Sentiment analysis of documents holds great importance in today's world, when numerous information is stored in databases and in the world wide web. An efficient algorithm to illicit such information, would be beneficial for social, economic as well as medical purposes. In this project, we have developed an algorithm to classify a document into positive or negative. Using our algorithm, we obtained a feature set from the data, and classified the documents based on this feature set. It is important to note that, in the classification, we have not used the independence assumption, which is considered by many procedures like the Naive Bayes. This makes the algorithm more general in scope. Moreover, because of the sparsity and high dimensionality of such data, we did not use empirical distribution for estimation, but developed a method by finding degree of close clustering of the data points. We have applied our algorithm on a movie review data set obtained from IMDb and obtained satisfactory results.

Keywords: sentiment, Run's Test, cross validation, higher dimensional pmf estimation

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Authors: Sushma Ghogale

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With a surge in user-generated content or feedback or reviews on the internet, it has become possible and important to know consumers' opinions about products and services. This data is important for both potential customers and businesses providing the services. Data from social media is attracting significant attention and has become the most prominent channel of expressing an unregulated opinion. Prospective customers look for reviews from experienced customers before deciding to buy a product or service. Several websites provide a platform for users to post their feedback for the provider and potential customers. However, the biggest challenge in analyzing such data is in extracting latent features and providing term-level analysis of the data. This paper proposes an approach to use topic modeling to classify the reviews into topics and conduct sentiment analysis to mine the opinions. This approach can analyse and classify latent topics mentioned by reviewers on business sites or review sites, or social media using topic modeling to identify the importance of each topic. It is followed by sentiment analysis to assess the satisfaction level of each topic. This approach provides a classification of hotel reviews using multiple machine learning techniques and comparing different classifiers to mine the opinions of user reviews through sentiment analysis. This experiment concludes that Multinomial Naïve Bayes classifier produces higher accuracy than other classifiers.

Keywords: latent Dirichlet allocation, topic modeling, text classification, sentiment analysis

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Authors: Guoyu Lin

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This paper explores the role investor sentiment plays in the relationship between analyst forecast dispersion and stock returns. With short sale constraints, stock prices are determined by the optimistic investors. During the high sentiment periods when investors suffer more from psychological bias, there are more optimistic investors. This is the first paper to document that following the high sentiment periods, stocks with the most analyst forecast dispersion are overpriced, earning significantly negative returns, while those with the least analyst forecast dispersion are not overpriced as the degree of belief dispersion is low. However, following the low sentiment periods, both are not overpriced. A portfolio which longs the least dispersed stocks and shorts the most dispersed stocks yields significantly positive returns only following the high sentiment periods. My findings can potentially reconcile the puzzling risk effect and mispricing effect in the literature. The risk (mispricing) effect suggests a positive (negative) relation between analyst forecast dispersion and future stock returns. Presumably, the magnitude of the mispricing effect depends on the proportion of irrational investors and their bias, which is positively related to investor sentiment. During the high sentiment period, the mispricing effect takes over and the overall effect is negative. During the low sentiment period, the percentage of irrational investors is mediate, and the mispricing effect and the risk effect counter each other, leading to insignificant relation.

Keywords: analyst forecast dispersion, short-sale constraints, investor sentiment, stock returns

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Authors: Xinyuan Liu, Xiaojun Jing, Yuan He, Junsheng Mu

Abstract:

Aspect-based Sentiment Analysis (ABSA) has attracted much attention due to its capacity to determine the sentiment polarity of the certain aspect in a sentence. In previous works, great significance of the interaction between aspect and sentence has been exhibited in ABSA. In consequence, a Multi-Level Attentional Networks (MLAN) is proposed. MLAN consists of four parts: Embedding Layer, Encoding Layer, Multi-Level Attentional (MLA) Layers and Final Prediction Layer. Among these parts, MLA Layers including Aspect Level Attentional (ALA) Layer and Interactive Attentional (ILA) Layer is the innovation of MLAN, whose function is to focus on the important information and obtain multiple levels’ attentional weighted representation of aspect and sentence. In the experiments, MLAN is compared with classical TD-LSTM, MemNet, RAM, ATAE-LSTM, IAN, AOA, LCR-Rot and AEN-GloVe on SemEval 2014 Dataset. The experimental results show that MLAN outperforms those state-of-the-art models greatly. And in case study, the works of ALA Layer and ILA Layer have been proven to be effective and interpretable.

Keywords: deep learning, aspect-based sentiment analysis, attention, natural language processing

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Authors: C. -L. Huang, J. -H. Ho

Abstract:

Social media has become more and more important in our life. Many enterprises promote their services and products to fans via the social media. The positive or negative sentiment of feedbacks from fans is very important for enterprises to improve their products, services, and promotion activities. The purpose of this paper is to understand the sentiment of the fan’s responses by analyzing the responses posted by fans on Facebook. The entity and aspect of fan’s responses were analyzed based on a predefined ontology. The ontology for cell phone sentiment analysis consists of aspect categories on the top level as follows: overall, shape, hardware, brand, price, and service. Each category consists of several sub-categories. All aspects for a fan’s response were found based on the ontology, and their corresponding sentimental terms were found using lexicon-based approach. The sentimental scores for aspects of fan responses were obtained by summarizing the sentimental terms in responses. The frequency of 'like' was also weighted in the sentimental score calculation. Three famous cell phone fan pages on Facebook were selected as demonstration cases to evaluate performances of the proposed methodology. Human judgment by several domain experts was also built for performance comparison. The performances of proposed approach were as good as those of human judgment on precision, recall and F1-measure.

Keywords: opinion mining, ontology, sentiment analysis, text mining

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Authors: Othmane Touri, Sanaa El Filali, El Habib Benlahmar

Abstract:

Natural Language Processing (NLP) has gained significant attention lately. It has proved its ability to analyze and extract insights from unstructured text data in various languages. It is found that one of the most popular NLP applications is sentiment analysis which aims to identify the sentiment expressed in a piece of text, such as positive, negative, or neutral, in multiple languages. While there are several multilingual NLP models available for sentiment analysis, there is a need to investigate their effectiveness in different contexts and applications. In this study, we aim to investigate the effectiveness of different multilingual NLP models for sentiment analysis on a dataset of online product reviews in multiple languages. The performance of several NLP models, including Google Cloud Natural Language API, Microsoft Azure Cognitive Services, Amazon Comprehend, Stanford CoreNLP, spaCy, and Hugging Face Transformers are being compared. The models based on several metrics, including accuracy, precision, recall, and F1 score, are being evaluated and compared to their performance across different categories of product reviews. In order to run the study, preprocessing of the dataset has been performed by cleaning and tokenizing the text data in multiple languages. Then training and testing each model has been applied using a cross-validation approach where randomly dividing the dataset into training and testing sets and repeating the process multiple times has been used. A grid search approach to optimize the hyperparameters of each model and select the best-performing model for each category of product reviews and language has been applied. The findings of this study provide insights into the effectiveness of different multilingual NLP models for Multilingual Sentiment Analysis and their suitability for different languages and applications. The strengths and limitations of each model were identified, and recommendations for selecting the most performant model based on the specific requirements of a project were provided. This study contributes to the advancement of research methods in multilingual NLP and provides a practical guide for researchers and practitioners in the field.

Keywords: NLP, multilingual, sentiment analysis, texts

Procedia PDF Downloads 60