Search results for: patent sentiment analysis

Authors: Seonjae Lee, Sungjoo Lee

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Excavation is necessary to remain competitive in the current business environment. The company survived the rapidly changing industry conditions by adapting new business strategy and reducing technology challenges. Traditionally, the two methods are conducted excavations for new businesses. The first method is, qualitative analysis of expert opinion, which is gathered through opportunities and secondly, new technologies are discovered through quantitative data analysis of method patents. The second method increases time and cost. Patent data is restricted for use and the purpose of discovering business opportunities. This study presents the company's characteristics (sector, size, etc.), of new business opportunities in customized form by reviewing the value chain perspective and to contributing to creating new business opportunities in the proposed model. It utilizes the trademark database of the Korean Intellectual Property Office (KIPO) and proprietary company information database of the Korea Enterprise Data (KED). This data is key to discovering new business opportunities with analysis of competitors and advanced business trademarks (Module 1) and trading analysis of competitors found in the KED (Module 2).

Keywords: value chain, trademark, trading analysis, new business opportunity

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Authors: Fernando C. Labouriau, Ricardo M. Naveiro

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This paper presents a model for mapping the evolutive pattern of products in order to generate new ideas, to perceive emerging technologies and to manage product’s portfolios in new product development (NPD). According to the proposed model, the information extracted from the patent system is filtered and analyzed with TRIZ tools to produce the input information to the NPD process. The authors acknowledge that the NPD process is well integrated within the enterprises business strategic planning and that new products are vital in the competitive market nowadays. In the other hand, it has been observed the proactive use of patent information in some methodologies for selecting projects, mapping technological change and generating product concepts. And one of these methodologies is TRIZ, a theory created to favor innovation and to improve product design that provided the analytical framework for the model. Initially, it is presented an introduction to TRIZ mainly focused on the patterns of evolution of technical systems and its strategic uses, a brief and absolutely non-comprehensive description as the theory has several others tools being widely employed in technical and business applications. Then, it is introduced the model for mapping the products evolutive pattern with its three basic pillars, namely patent information, TRIZ and NPD, and the methodology for implementation. Following, a case study of a Brazilian bike manufacturing is presented to proceed the mapping of a product evolutive pattern by decomposing and analyzing one of its assemblies along ten evolution lines in order to envision opportunities for further product development. Some of these lines are illustrated in more details to evaluate the features of the product in relation to the TRIZ concepts using a comparison perspective with patents in the state of the art to validate the product’s evolutionary potential. As a result, the case study provided several opportunities for a product improvement development program in different project categories, identifying technical and business impacts as well as indicating the lines of evolution that can mostly benefit from each opportunity.

Keywords: product development, patents, product strategy, systems evolution

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Authors: A. Ashraff

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The increasing importance of the web as a medium for electronic and business transactions has served as a catalyst or rather a driving force for the introduction and implementation of recommender systems. Recommender Systems play a major role in processing and analyzing thousands of data rows or reviews and help humans make a purchase decision of a product or service. It also has the ability to predict whether a particular user would rate a product or service based on the user’s profile behavioral pattern. At present, Recommender Systems are being used extensively in every domain known to us. They are said to be ubiquitous. However, in the field of recruitment, it’s not being utilized exclusively. Recent statistics show an increase in staff turnover, which has negatively impacted the organization as well as the employee. The reasons being company culture, working flexibility (work from home opportunity), no learning advancements, and pay scale. Further investigations revealed that there are lacking guidance or support, which helps a job seeker find the company that will suit him best, and though there’s information available about companies, job seekers can’t read all the reviews by themselves and get an analytical decision. In this paper, we propose an approach to study the available review data on IT companies (score their reviews based on user review sentiments) and gather information on job seekers, which includes their Psychometric evaluations. Then presents the job seeker with useful information or rather outputs on which company is most suitable for the job seeker. The theoretical approach, Algorithmic approach and the importance of such a system will be discussed in this paper.

Keywords: psychometric tests, recommender systems, sentiment analysis, hybrid recommender systems

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Authors: Chung Danbi, Linton Oliver, Whang Yoon-Jae

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Supermodularity, or complementarity, is a popular concept in economics which can characterize many objective functions such as utility, social welfare, and production functions. Further, supermodular dominance captures a preference for greater interdependence among inputs of those functions, and it can be applied to examine which input set would produce higher expected utility, social welfare, or production. Therefore, we propose and justify a consistent testing for a useful implication of supermodular dominance. We also conduct Monte Carlo simulations to explore the finite sample performance of our test, with critical values obtained from the recentered bootstrap method, with and without the selective recentering, and the subsampling method. Under various parameter settings, we confirmed that our test has reasonably good size and power performance. Finally, we apply our test to compare the geographic and distant knowledge spillover in terms of their effects on social welfare using the National Bureau of Economic Research (NBER) patent data. We expect localized citing to supermodularly dominate distant citing if the geographic knowledge spillover engenders greater social welfare than distant knowledge spillover. Taking subgroups based on firm and patent characteristics, we found that there is industry-wise and patent subclass-wise difference in the pattern of supermodular dominance between localized and distant citing. We also compare the results from analyzing different time periods to see if the development of Internet and communication technology has changed the pattern of the dominance. In addition, to appropriately deal with the sparse nature of the data, we apply high-dimensional methods to efficiently select relevant data.

Keywords: supermodularity, supermodular dominance, stochastic dominance, Monte Carlo simulation, bootstrap, subsampling

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Authors: Asma Ameur, Dhafer Malouche

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Nowadays, user comments on the Internet have an important impact on hotel bookings. This confirms that the e-reputation issue can influence the likelihood of customer loyalty to a hotel. In this way, e-reputation has become a real differentiator between hotels. For this reason, we have a unique opportunity in the opinion mining field to analyze the comments. In fact, this field provides the possibility of extracting information related to the polarity of user reviews. This sentimental study (Opinion Mining) represents a new line of research for analyzing the unstructured textual data. Knowing the score of e-reputation helps the hotelier to better manage his marketing strategy. The score we then obtain is translated into the image of hotels to differentiate between them. Therefore, this present research highlights the importance of hotel satisfaction ‘scoring. To calculate the satisfaction score, the sentimental analysis can be manipulated by several techniques of machine learning. In fact, this study treats the extracted textual data by using the Artificial Neural Networks Approach (ANNs). In this context, we adopt the aforementioned technique to extract information from the comments available in the ‘Trip Advisor’ website. This actual paper details the description and the modeling of the ANNs approach for the scoring of online hotel reviews. In summary, the validation of this used method provides a significant model for hotel sentiment analysis. So, it provides the possibility to determine precisely the polarity of the hotel users reviews. The empirical results show that the ANNs are an accurate approach for sentiment analysis. The obtained results show also that this proposed approach serves to the dimensionality reduction for textual data’ clustering. Thus, this study provides researchers with a useful exploration of this technique. Finally, we outline guidelines for future research in the hotel e-reputation field as comparing the ANNs with other technique.

Keywords: clustering, consumer behavior, data mining, e-reputation, machine learning, neural network, online hotel ‘reviews, opinion mining, scoring

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Authors: Zhi-Wei Zheng, Zhong-Qi Liu, Jia-Ling Qiu, Shan-Qing Guo, Zhong-Wei Jia, Chun Hao

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Objectives: The purpose of this study was to understand and examine the association between diurnal mood variation and sexual/health-related status among men who have sex with men (MSM) using data from MSM Chinese Twitter messages. The study consists of 843,745 postings of 377,610 MSM users located in Guangdong that were culled from the MSM Chinese Twitter App. Positive affect, negative affect, sexual related behaviors, and health-related status were measured using the Simplified Chinese Linguistic Inquiry and Word Count. Emotions, including joy, sadness, anger, fear, and disgust were measured using the Weibo Basic Mood Lexicon. A positive sentiment score and a positive emotions score were also calculated. Linear regression models based on a permutation test were used to assess associations between affective states and sexual/health-related status. In the results, 5,871 active MSM users and their 477,374 postings were finally selected. MSM expressed positive affect and joy at 8 a.m. and expressed negative affect and negative emotions between 2 a.m. and 4 a.m. In addition, 25.1% of negative postings were directly related to health and 13.4% reported seeking social support during that sensitive period. MSM who were senior, educated, overweight or obese, self-identified as performing a versatile sex role, and with less followers, more followers, and less chat groups mainly expressed more negative affect and negative emotions. MSM who talked more about sexual-related behaviors had a higher positive sentiment score (β=0.29, p < 0.001) and a higher positive emotions score (β = 0.16, p < 0.001). MSM who reported more on their health status had a lower positive sentiment score (β = -0.83, p < 0.001) and a lower positive emotions score (β = -0.37, p < 0.001). The study concluded that psychological intervention based on an app for MSM should be conducted, as it may improve mental health.

Keywords: affect, men who have sex with men, sexual related behavior, health-related status, social media

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Authors: Maryam Heidari, James H. Jones Jr.

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Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event or product. However, this use raises an important question: what percentage of information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a bot, instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. In this paper, we introduce a model for social media bot detection which uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features. Our use of a Natural Language Processing approach to derive topic-independent features for our new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data as generated by a bot or a human, where the most accurate prior work achieved accuracy of 92\%.

Keywords: bot detection, natural language processing, neural network, social media

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Authors: Joao Ferreira Do Rosario, Maria De Lurdes Calisto, Ana Teresa Machado, Nuno Gustavo, Rui Gonçalves

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The tourism sector is increasingly important to the economic performance of countries and a relevant theme to academic research, increasing the importance of understanding how and why tourists evaluate tourism locations. The city of Lisbon is currently a tourist destination of excellence in the European and world-wide panorama, registering a significant growth of the economic weight of its tourist activities in the Gross Added Value of the region. Although there is research on the feedback of those who visit tourist sites and different methodologies for studying tourist sites have been applied, this research seeks to be innovative in the objective of obtaining insights on the competitiveness in terms of attractiveness of the city of Lisbon as a tourist destination, based the feedback of tourists in the Facebook pages of the most visited museums and monuments of Lisbon, an interpretation that is relevant in the development of strategies of tourist attraction. The intangible dimension of the tourism offer, due to its unique condition of simultaneous production and consumption, makes eWOM particularly relevant. The testimony of consumers is thus a decisive factor in the decision-making and buying process in tourism. Online social networks are one of the most used platforms for tourists to evaluate the attractiveness's points of a tourism destination (e.g. cultural and historical heritage), with this user-generated feedback enabling relevant information about the customer-tourists. This information is related to the tourist experience representing the true voice of the customer. Furthermore, this voice perceived by others as genuine, opposite to marketing messages, may have a powerful word-of-mouth influence on other potential tourists. The relevance of online reviews sharing, however, becomes particularly complex, considering social media users’ different profiles or the possible and different sources of information available, as well as their associated reputation associated with each source. In the light of these trends, our research focuses on the tourists’ feedback on Facebook pages of the most visited museums and monuments of Lisbon that contribute to its attractiveness as a tourism destination. Sentiment Analysis is the methodology selected for this research, using public available information in the online context, which was deemed as an appropriate non-participatory observation method. Data will be collected from two museums (Museu dos Coches and Museu de Arte Antiga) and three monuments ((Mosteiro dos Jerónimos, Torre de Belém and Panteão Nacional) Facebook pages during a period of one year. The research results will help in the evaluation of the considered places by the tourists, their contribution to the city attractiveness and present insights helpful for the management decisions regarding this museums and monuments. The results of this study will also contribute to a better knowledge of the tourism sector, namely the identification of attributes in the evaluation and choice of the city of Lisbon as a tourist destination. Further research will evaluate the Lisbon attraction points for tourists in different categories beyond museums and monuments, will also evaluate the tourist feedback from other sources like TripAdvisor and apply the same methodology in other cities and country regions.

Keywords: Lisbon tourism, opinion mining, sentiment analysis, tourism location attractiveness evaluation

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Authors: Rosdyana Mangir Irawan Kusuma, Wei-Chun Kao, Ho-Thi Trang, Yu-Yen Ou, Kai-Lung Hua

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Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively.

Keywords: candlestick chart, deep learning, neural network, stock market prediction

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Authors: Heeyong Noh, Seongryong Kang, Sungjoo Lee

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As the world becomes increasingly interconnected, firms have tried to explore market opportunities not only in the domestic market but also abroad. In particular, transactions of intangible assets in the global market now take on great importance. Accordingly, technology transfer activities such as patent licensing, copyright transfer, or workforce trainings which are considered significant to leverage an organization’s internal capabilities, are occurring more frequently and briskly across the world than ever before. Though a number of studies have addressed the issues regarding technology transfer, most of them have focused on university-industry technology transfer. Of course, some have investigated international technology transfer phenomenon but used patent citations data as a proxy. In order to understand the phenomena more clearly, it would be necessary to collect and analyze data that can measure technology transfer activities between firms more directly. Therefore, this study aims to examine the patterns of international trade in technology by employing data about international technology in-licensing activities in Korean firms. We also investigate the effect of international technology in-licensing strategy on a firm’s innovation performance. The research findings are expected to help R&D managers understand how firms have absorbed technological knowledge from foreign firms in the form of licensing and further develop effective international collaboration strategies. In addition, significant implications can be offered for political decision-making regarding technology trade within increasing international interconnections.

Keywords: international technology trade, technology trade effect, technology transfer, R&D managers

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Authors: Matthew John F. Sino Cruz, Sarah Jane M. Ferrer, Janice C. Francisco

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COVID-19 pandemic has affected more than 114 countries all over the world since it was considered a global health concern in 2020. Different sectors, including education, have shifted to remote/distant setups to follow the guidelines set to prevent the spread of the disease. One of the higher education institutes which shifted to remote setup is the Pangasinan State University (PSU). In order to continue providing quality instructions to the students, PSU designed Flexible Learning Model to still provide services to its stakeholders amidst the pandemic. The model covers the redesigning of delivering instructions in remote setup and the technology needed to support these adjustments. The primary goal of this study is to determine the insights of the PSU – Bayambang students towards the remote setup implemented during the pandemic and how they perceived the initiatives employed in relation to their experiences in flexible learning. In this study, the topic modelling approach was implemented using Latent Dirichlet Allocation. The dataset used in the study. The results show that the most common concern of the students includes time and resource management, poor internet connection issues, and difficulty coping with the flexible learning modality. Furthermore, the findings of the study can be used as one of the bases for the administration to review and improve the policies and initiatives implemented during the pandemic in relation to remote service delivery. In addition, further studies can be conducted to determine the overall sentiment of the other stakeholders in the policies implemented at the University.

Keywords: COVID-19, topic modelling, students’ sentiment, flexible learning, Latent Dirichlet allocation

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Authors: Gemma García López

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The goal of this study is to analyze if search queries carried out in search engines such as Google, can offer emotional information about the user that performs them. Knowing the emotional state in which the Internet user is located can be a key to achieve the maximum personalization of content and the detection of worrying behaviors. For this, two studies were carried out using tools with advanced natural language processing techniques. The first study determines if a query can be classified as positive, negative or neutral, while the second study extracts emotional content from words and applies the categorical and dimensional models for the representation of emotions. In addition, we use search queries in Spanish and English to establish similarities and differences between two languages. The results revealed that text search queries performed by users on the Internet can be classified emotionally. This allows us to better understand the emotional state of the user at the time of the search, which could involve adapting the technology and personalizing the responses to different emotional states.

Keywords: emotion classification, text search queries, emotional analysis, sentiment analysis in text, natural language processing

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Authors: Sofía Luna, Carla González Salamanca

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The 2008 economic crisis had huge implications in Europe. In this continent, the repercussions of the crisis were not only economic but also political and institutional. The economic stress has generated changes in the perception of the citizens, their attitude and the confidence placed in the political organizations. The lost of confidence is not only present in the debtor countries but it is also present in the European economic powers like Germany and France. This research explains how the economic crisis had an impact in the identity, population’s attitude and how this generated the rise of extreme right parties. In addition, it defines the different types of attitudes and support that exist towards these political and economic institutions. The results of this investigation show that the depression beside of its economic implications, it caused institutional, social and political difficulties for the Union. Moreover, the support and attitudes of the population were severely strained because the confidence in the political organization decreased. Furthermore, a rise in the otherness sentiment was shown. In other words, the distinction between “us” and “them” increased causing repercussions in the collective European identity. Additionally, there was a spread in national identities that caused the rise of the extreme right wing parties. In conclusion, the 2008 economic crisis caused not only economic stress but also it generated a political, social and institutional crisis in Europe.

Keywords: Europe, identity, economic crisis, otherness sentiment

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Authors: Priscilla Akua Agyapong

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Background: In a country of nearly 34 million people, Ghana suffers from rapidly growing pediatric CHD cases and not enough pediatric specialists to attend to the burgeoning needs of these children. Most of the cases are either missed or diagnosed late, resulting in increased mortality. According to the National Cardiothoracic Centre, 1 in every 100,000 births in Ghana has CHD; however, there is limited data on the clinical presentation and its management, one of the many reasons I decided to do this case study coupled with the loss my 2 month old niece to multiple Ventricular Septal Defect 3 years ago due late diagnoses. Method: A retrospective cohort study was performed at the child health clinic of one of Ghana’s public tertiary Institutions using data from their electronic health record (EHR) from February 2021 to April 2022. All suspected or provisionally diagnosed cases were included in the analysis. Results: Records of over 3000 children were reviewed with an approximate male to female ratio of 1:1.53 cases diagnosed during the period of study, most of whom were less than 5 years of age. 25 cases had complete clinical records, with acyanotic septal defects being the most diagnosed. 62.5% of the cases were ventricular septal defects, followed by Patent Ductus Arteriosus (23%) and Atrial Septal Defects (4.5%). Tetralogy of Fallot was the most predominant and complex cyanotic CHD with 10%. Conclusion: The indeterminate coronary anatomy of infants makes it difficult to use only echocardiography and other conventional clinical methods in screening for CHDs. There are rising modernizations and new innovative ways that can be employed in Ghana for early detection, hence preventing the delay of a potential surgical repair. It is, therefore, imperative to create the needed awareness about these “SILENT CRISES” and help save the Ghanaian child’s life.

Keywords: congenital heart defect(CHD), ventricular septal defect(VSD), atrial septal defect(ASD), patent ductus arteriosus(PDA)

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Authors: Xu Wang

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This research builds linear regressions of U.S. macroeconomic condition and volatility measures in the investment grade and high yield Credit Default Swap index spreads using monthly data from March 2009 to July 2016, to study the relationship between different dimensions of macroeconomy and overall credit risk quality. The most significant contribution of this research is systematically examining individual and joint effects of macroeconomic condition and volatility on CDX spreads by including macroeconomic time series that captures different dimensions of the U.S. economy. The industrial production index growth, non-farm payroll growth, consumer price index growth, 3-month treasury rate and consumer sentiment are introduced to capture the condition of real economic activity, employment, inflation, monetary policy and risk aversion respectively. The conditional variance of the macroeconomic series is constructed using ARMA-GARCH model and is used to measure macroeconomic volatility. The linear regression model is conducted to capture relationships between monthly average CDX spreads and macroeconomic variables. The Newey–West estimator is used to control for autocorrelation and heteroskedasticity in error terms. Furthermore, the sensitivity factor analysis and standardized coefficients analysis are conducted to compare the sensitivity of CDX spreads to different macroeconomic variables and to compare relative effects of macroeconomic condition versus macroeconomic uncertainty respectively. This research shows that macroeconomic condition can have a negative effect on CDX spread while macroeconomic volatility has a positive effect on determining CDX spread. Macroeconomic condition and volatility variables can jointly explain more than 70% of the whole variation of the CDX spread. In addition, sensitivity factor analysis shows that the CDX spread is the most sensitive to Consumer Sentiment index. Finally, the standardized coefficients analysis shows that both macroeconomic condition and volatility variables are important in determining CDX spread but macroeconomic condition category of variables have more relative importance in determining CDX spread than macroeconomic volatility category of variables. This research shows that the CDX spread can reflect the individual and joint effects of macroeconomic condition and volatility, which suggests that individual investors or government should carefully regard CDX spread as a measure of overall credit risk because the CDX spread is influenced by macroeconomy. In addition, the significance of macroeconomic condition and volatility variables, such as Non-farm Payroll growth rate and Industrial Production Index growth volatility suggests that the government, should pay more attention to the overall credit quality in the market when macroecnomy is low or volatile.

Keywords: autoregressive moving average model, credit spread puzzle, credit default swap spread, generalized autoregressive conditional heteroskedasticity model, macroeconomic conditions, macroeconomic uncertainty

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Authors: T. Rakhi, Thrivikram Shenoy

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Esophageal atresia is a disorder of maldevelopment of esophagus with or without a connection to the trachea. Radiological reviews are needed in consultation with the pediatric surgeon and neonatologist and we report a rare case of esophageal atresia associated with atrial septal defect-patent ductus arteriosus complex. A 2-day old female baby born at term, weighing 3.010kg, admitted to the Neonatal Intensive Care Unit with respiratory distress and excessive oral secretions. On examination, continuous murmur and cyanosis were seen. Esophageal atresia was suspected, after a failed attempt to pass a nasogastric tube. Chest radiograph showed coiling of the nasogastric tube and absent gas shadow in the abdomen. Echocardiography confirmed Patent Ductus Arteriosus with Atrial Septal Defect not in failure and was diagnosed with esophageal atresia with suspected fistula posted for surgical repair. After preliminary management with oxygenation, suctioning in prone position and antibiotics, investigations revealed Hb 17gms serum biochemistry, coagulation profile and C-Reactive Protein Test normal. The baby was premedicated with 5mcg of fentanyl and 100 mcg of midazolam and a rapid awake laryngoscopy was done to rule out difficult airway followed by induction with o2 air, sevo and atracurium 2 mg. Placement of a 3.5 tube was uneventful at first attempt and after confirming bilateral air entry positioned in the lateral position for Right thoracotomy. A pulse oximeter, Echocardiogram, Non-invasive Blood Pressure, temperature and a precordial stethoscope in left axilla were essential monitors. During thoracotomy, both the ends of the esophagus and the fistula could not be located after thorough search suggesting an on table finding of type A esophageal atresia. The baby was repositioned for gastrostomy, and cervical esophagostomy ventilated overnight and extubated uneventful. Absent gas shadow was overlooked and the purpose of this presentation is to create an awareness between the neonatologist, pediatric surgeons and anesthesiologist regarding variation of typing of Tracheoesophageal fistula pre and intraoperatively. A need for imaging modalities warranted for a definitive diagnosis in the presence of a gasless stomach.

Keywords: anesthetic, atrial septal defects, esophageal atresia, patent ductus arteriosus, perioperative, chest x-ray

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Authors: Zdzislaw Piotr Kaminski, Miroslaw Wendeker, Zbigniew Czyz

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The paper describes the geometric model, calculation algorithm and results of the CFD simulation of the airflow around a rotor in the vertical axis wind turbine (VAWT) with the ANSYS Fluent computational solver. The CFD method enables creating aerodynamic characteristics of forces acting on rotor working surfaces and determining parameters such as torque or power generated by the rotor assembly. The object of the research was a rotor whose construction is based on patent no.PL219985. The conducted tests enabled a mathematical model with a description of the generation of aerodynamic forces acting on each rotor blade. Additionally, this model was compared to the results of the wind tunnel tests. The analysis also focused on the influence of the blade angle on turbine power and the TSR. The research has shown that the turbine blade angle has a significant impact on the optimal value of the TSR.

Keywords: computational fluid dynamics, numerical analysis, renewable energy, wind turbine

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Authors: Cherif Y., Ghariani R., Derbal S., Farhati S., Ben Dahmen F., Abdallah M.

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Introduction: Tuberculosis (TBC) is a frequent infection and is still a major public health problem in Tunisia. The aim of this work is to focus on diagnostic and therapeutic characteristics of TBC in patients referred to our internal medicine department. Patients and Methods: The study was retrospective and descriptive of a cohort of consecutive cases treated from January 2016 to December 2019, collecting patients with latent or patent TBC. Twenty-eight medical records of adults diagnosed with TBC were reviewed. Results: Twenty-eight patients, including 18 women and 10 men, were diagnosed with TBC. Their mean age is 48 years (range: 22-78 years). Five patients have a medical history of diabetes mellitus, 1 patient was followed for systemic lupus erythematosus treated with corticosteroids and immunosuppressant drugs, and another was treated with corticosteroids for Mac Duffy syndrome. The TBC is latent in 12 cases and patent in 16 cases. The most common symptoms were fever and weight loss and were found in 10 cases, a cough in 2 cases, sputum in 3 cases, lymph nodes in 4 cases, erythema nodosum in 2 cases, and neurological signs in 3 cases. Lymphopenia is noticed in 3 cases and a biological inflammatory syndrome in 18 of the cases. The purified protein derivate reaction was positive in 17 cases, anergic in 3 cases, negative in 5 cases, and not done in 3 cases. The acid-fast bacilli stain culture was strongly positive in one patient. The histopathological study was conclusive in 11 patients and showed granulomatosis with caseous necrosis. TBC was pulmonary in 7 patients, lymph node in 7 cases, peritoneal in 7 cases, digestive in 1 case, neuromeningeal in 3 cases, and thyroïd in 1 case. Seven patients had multifocal TBC. All the patients received anti-tuberculosis treatment with a mean duration of 8 months with no failure or relapse with an average follow-up time of 10.58 months. Conclusion: Diagnosis and management of TBC remain essential to avoid serious complications. The survey is necessary to ensure timely detection and treatment of infected adults to decrease its incidence. The best treatment remains preventive through vaccination and improving social and economic conditions.

Keywords: tuberculosis, infection, autoimmune disease, granulomatosis

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Authors: E. Mike Dison, T. Pathinathan

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Computing with Words (CWW) and Possibilistic Relational Universal Fuzzy (PRUF) are the two concepts which widely represent and measure the vaguely defined natural phenomenon. In this paper, we study the positional alteration of the phrases by which the impact of a natural language proposition gets affected and/or modified. We observe the gradations due to sensitivity/feeling of a statement towards the positional alterations. We derive the classification and modification of the meaning of words due to the positional alteration. We present the results with reference to set theoretic interpretations.

Keywords: appositive, computing with words, possibilistic relational universal fuzzy (PRUF), semantic sentiment analysis, set-theoretic interpretations

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Authors: Andrew Wee

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As a result of the Italian Independence Wars starting in 1848, Italy began to change through unification. People gradually moved away from some of their traditional practices and values, such as the long-held belief that women were inferior to men, as part of the Risorgimento. Italians began to take interest in opera as a form of emotional release. As opera became more popular and prominent in their culture, it aided in the dissemination of ideas, especially stimulating the spread of imperialism, in the late 19th century, as Italy began extending its presence to other countries. In order to collect the information needed to analyze Italy’s foreign presence, it was necessary to consult texts concerning the culture of the Risorgimento. These texts included primary sources from operatic composers and contemporary recorded accounts. Letters from Giuseppe Verdi, a leader in opera during the Risorgimento, have been scrutinized for indications of popular attitudes of the time. The cultural context of the Risorgimento is essential to understanding the Italian motives and attitudes towards the outside world. On the more political side, research has also entailed the study of historical data of general laws, policies, and their purposes concerning geopolitical boundaries and foreign affairs, such as Edward Said’s thesis on Orientalism. By establishing these two characteristics of Italy, the paper will thoroughly illustrate Italy’s presence in foreign affairs. Texts have been searched with the intent of using information that reveals Italian attitudes toward exotic countries to determine whether their demeanor was positive or condescending. Motives behind sources have been interpreted in context in order to form a complete picture of the Italian sentiment towards foreigners. Additionally, research pertaining to Italian nationalism and imperialism such as song and literature has been used. The primary form of research has been the division of sources that are culturally based and those that are political in nature. Opera had always been developing since its creation in the 17th century, and in the 19th century, the bel canto movement revolutionized opera and its role in Italian society. This paper uses evidence that popular sentiment was influenced by opera to support the belief that the evolution of opera was as a result of the nationalist sentiment, and in turn fueled the cultural movement known as the Risorgimento. In this way, opera proceeded to affect Italian culture by spreading the idea of imperialism.

Keywords: opera, Italian unification, music history, imperialism

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Authors: Ezgi Şendil

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Information extracted from social media has received great attention since it has become an effective alternative for collecting people’s opinions and emotions based on specific experiences in a faster and easier way. The paper aims to put data in a meaningful way to analyze users’ posts and get a result in terms of the experiences and opinions of the users during and after natural disasters. The posts collected from Reddit are classified into nine different categories, including injured/dead people, infrastructure and utility damage, missing/found people, donation needs/offers, caution/advice, and emotional support, identified by using labelled Twitter data and four different machine learning (ML) classifiers.

Keywords: disaster, NLP, postdisaster management, sentiment analysis

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Authors: Luisa Müting, Wisnu Harto Adiwijoyo

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Agroforestry systems transform the agricultural landscapes in the Sahel region of Africa, providing food and farming products consumed for subsistence or sold for income. In the incrementally dry climate of the Sahel region, the spreading of agroforestry practices is integral for policymaker efforts to counteract land degradation and provide soil restoration in the region. Several measures on agroforestry practices have been implemented in the region by governmental and non-governmental institutions in recent years. However, despite the efforts, past research shows that awareness of how policies and interventions are being consumed and perceived by the public remains low. Therefore, interpreting public policy dilemmas by analyzing the public perception regarding agroforestry concepts and practices is necessary. Public perceptions and discourses can be an essential driver or constraint for the adoption of agroforestry practices in the region. Thus, understanding the public discourse behavior of crucial stakeholders could assist policymakers in developing inclusive and contextual policies that are relevant to the context of agroforestry adoption in Sahel region. To answer how information about agroforestry spreads and is perceived by the public. As internet usage increased drastically over the past decade, reaching a share of 33 percent of the population being connected to the internet, this research is based on online conversation data. Social media data from Facebook are gathered daily between April 2021 and April 2022 in Djibouti, Senegal, Mali, and Nigeria based on their share of active internet users compared to other countries in the Sahel region. A systematic methodology was applied to the extracted social media using discourse network analysis (DNA). This study then clustered the data by the types of agroforestry practices, sentiments, and country. Additionally, this research extracted the text data from online news media during the same period to pinpoint events related to the topic of agroforestry. The preliminary result indicates that tree management, crops, and livestock integration, diversifying species and genetic resources, and focusing on interactions and productivity across the agricultural system; are the most notable keywords in agroforestry-related conversations within the four countries in the Sahel region. Additionally, approximately 84 percent of the discussions were still dominated by big actors, such as NGO or government actors. Furthermore, as a subject of communication within agroforestry discourse, the Great Green Wall initiative generates almost 60 percent positive sentiment within the captured social media data, effectively having a more significant outreach than general agroforestry topics. This study provides an understanding for scholars and policymakers with a springboard for further research or policy design on agroforestry in the four countries of the Sahel region with systematically uncaptured novel data from the internet.

Keywords: sahel, djibouti, senegal, mali, nigeria, social networks analysis, public discourse analysis, sentiment analysis, content analysis, social media, online news, agroforestry, land restoration

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Authors: Taylan Kabbani, Ekrem Duman

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The design of adaptive systems that take advantage of financial markets while reducing the risk can bring more stagnant wealth into the global market. However, most efforts made to generate successful deals in trading financial assets rely on Supervised Learning (SL), which suffered from various limitations. Deep Reinforcement Learning (DRL) offers to solve these drawbacks of SL approaches by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. In this paper, a continuous action space approach is adopted to give the trading agent the ability to gradually adjust the portfolio's positions with each time step (dynamically re-allocate investments), resulting in better agent-environment interaction and faster convergence of the learning process. In addition, the approach supports the managing of a portfolio with several assets instead of a single one. This work represents a novel DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem, or what is referred to as The Agent Environment as Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. More specifically, we design an environment that simulates the real-world trading process by augmenting the state representation with ten different technical indicators and sentiment analysis of news articles for each stock. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which can learn policies in high-dimensional and continuous action spaces like those typically found in the stock market environment. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of deep reinforcement learning in financial markets over other types of machine learning such as supervised learning and proves its credibility and advantages of strategic decision-making.

Keywords: the stock market, deep reinforcement learning, MDP, twin delayed deep deterministic policy gradient, sentiment analysis, technical indicators, autonomous agent

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Authors: Kanika Sood, Sijie Shang

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A fundamental goal of education includes preparing students to become a part of the global workforce by making beneficial contributions to society. In this paper, we analyze student performance for multiple courses that involve different teaching pedagogies: a cooperative learning technique and an inquiry-based learning strategy. Student performance includes student engagement, grades, and attendance records. We perform this study in the Computer Science department for online and in-person courses for 450 students. We will perform correlation analysis to study the relationship between student scores and other parameters such as gender, mode of learning. We use natural language processing and machine learning to analyze student feedback data and performance data. We assess the learning outcomes of two teaching pedagogies for undergraduate and graduate courses to showcase the impact of pedagogical adoption and learning outcome as determinants of academic achievement. Early findings suggest that when using the specified pedagogies, students become experts on their topics and illustrate enhanced engagement with peers.

Keywords: bag-of-words, cooperative learning, education, inquiry-based learning, in-person learning, natural language processing, online learning, sentiment analysis, teaching pedagogy

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Authors: Levi Corallo

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Typed language made by humans on computers and cell phones has made a significant distinction from previous modes of written language exchanges. While acronyms remain one of the most predominant markings of typed language, another and perhaps more recent revolution in the way humans communicate has been with the use of symbols or glyphs, primarily Emojis—globally introduced on the iPhone keyboard by Apple in 2008. This paper seeks to analyze the use of symbols in typed communication from both a linguistic and machine learning perspective. The Unicode system will be explored and methods of encoding will be juxtaposed with the current machine and human perception. Topics in how typed symbol usage exists in conversation will be explored as well as topics across current research methods dealing with Emojis like sentiment analysis, predictive text models, and so on. This study proposes that sequential analysis is a significant feature for analyzing unicode characters in a corpus with machine learning. Current models that are trying to learn or translate the meaning of Emojis should be starting to learn using bi- and tri-grams of Emoji, as well as observing the relationship between combinations of different Emoji in tandem. The sociolinguistics of an entire new vernacular of language referred to here as ‘typed language’ will also be delineated across my analysis with unicode glyphs from both a semantic and technical perspective.

Keywords: unicode, text symbols, emojis, glyphs, communication

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Authors: Jamshid Qodirov, Sirojiddin Komolov, Ravilov Mirahmad, Olimjon Mirzayev

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Nowadays, natural language processing tools play a crucial role in our daily lives, including various techniques with text processing. There are very advanced models in modern languages, such as English, Russian etc. But, in some languages, such as Uzbek, the NLP models have been developed recently. Thus, there are only a few NLP models in Uzbek language. Moreover, there is no such work that could show which Uzbek NLP model behaves in different situations and when to use them. This work tries to close this gap and compares the Uzbek NLP models existing as of the time this article was written. The authors try to compare the NLP models in two different scenarios: sentiment analysis and sentence similarity, which are the implementations of the two most common problems in the industry: classification and similarity. Another outcome from this work is two datasets for classification and sentence similarity in Uzbek language that we generated ourselves and can be useful in both industry and academia as well.

Keywords: NLP, benchmak, bert, vectorization

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Authors: Martina Katalin Szabó, István Üveges

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In this paper, the usage of negative emotive words is examined on the basis of a large Hungarian twitter-database via NLP methods. The data is analysed from a gender point of view, as well as changes in language usage over time. The term negative emotive word refers to those words that, on their own, without context, have semantic content that can be associated with negative emotion, but in particular cases, they may function as intensifiers (e.g. rohadt jó ’damn good’) or a sentiment expression with positive polarity despite their negative prior polarity (e.g. brutális, ahogy ez a férfi rajzol ’it’s awesome (lit. brutal) how this guy draws’. Based on the findings of several authors, the same phenomenon can be found in other languages, so it is probably a language-independent feature. For the recent analysis, 67783 tweets were collected: 37818 tweets (19580 tweets written by females and 18238 tweets written by males) in 2016 and 48344 (18379 tweets written by females and 29965 tweets written by males) in 2021. The goal of the research was to make up two datasets comparable from the viewpoint of semantic changes, as well as from gender specificities. An exhaustive lexicon of Hungarian negative emotive intensifiers was also compiled (containing 214 words). After basic preprocessing steps, tweets were processed by ‘magyarlanc’, a toolkit is written in JAVA for the linguistic processing of Hungarian texts. Then, the frequency and collocation features of all these words in our corpus were automatically analyzed (via the analysis of parts-of-speech and sentiment values of the co-occurring words). Finally, the results of all four subcorpora were compared. Here some of the main outcomes of our analyses are provided: There are almost four times fewer cases in the male corpus compared to the female corpus when the negative emotive intensifier modified a negative polarity word in the tweet (e.g., damn bad). At the same time, male authors used these intensifiers more frequently, modifying a positive polarity or a neutral word (e.g., damn good and damn big). Results also pointed out that, in contrast to female authors, male authors used these words much more frequently as a positive polarity word as well (e.g., brutális, ahogy ez a férfi rajzol ’it’s awesome (lit. brutal) how this guy draws’). We also observed that male authors use significantly fewer types of emotive intensifiers than female authors, and the frequency proportion of the words is more balanced in the female corpus. As for changes in language usage over time, some notable differences in the frequency and collocation features of the words examined were identified: some of the words collocate with more positive words in the 2nd subcorpora than in the 1st, which points to the semantic change of these words over time.

Keywords: gender differences, negative emotive words, semantic changes over time, twitter

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Authors: Fernanda Conforto de Oliveira

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The International Monetary Fund (IMF) has a central role in global financial governance as the world’s leading crisis lender. Its practice of conditional lending – conditioning loans on the implementation of economic policy adjustments – is the primary lever by which the institution interacts with and influences the policy choices of member countries and has been a key topic of interest to scholars and public opinion. However, empirical evidence about the economic and (geo)political determinants of IMF lending behavior remains inconclusive, and no model that explains IMF policies has been identified. This research moves beyond panel analysis to focus on financial negotiations for the first IMF programs in Argentina and Brazil in the early post-war period. It seeks to understand why negotiations achieved distinct objectives: Argentinean officials cooperated and complied with IMF policies, whereas their Brazilian counterparts hesitated. Using qualitative and automated text analysis, this paper analyses the hypothesis about whether a differential IMF treatment could help to explain these distinct outcomes. This paper contributes to historical studies on IMF-Latin America relations and the broader literature in international policy economy about IMF policies.

Keywords: international monetary fund, international history, financial history, Latin American economic history, natural language processing, sentiment analysis

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Authors: José Manue Carvalho Vieira, Mariana Magalhães, Elizabeth Serra

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The Port Wine industry is essential to Portugal because it carries a tangible cultural heritage and for social and economic reasons. Positioned as a luxury product, brands need to pay more attention to the new generation's habits, preferences, languages, and sensory perceptions. Healthy lifestyles, anti-alcohol campaigns, and digitalisation of their buying decision process need to be better understood to understand the wine market in the future. The purpose of this study is to clarify the sensory perception gap between Port Wine descriptors promotion and the new generation's perceptions to help wineries to align their strategies. Based on the interpretivist approach - multiple methods and techniques (mixed-methods), different world views and different assumptions, and different data collection methods and analysis, this research integrated qualitative semi-structured interviews, Port Wine promotion contents, and social media perceptions mined by Sentiment Analysis Enginius algorithm. Findings confirm that Port Wine CEOs' strategies, brands' promotional content, and social perceptions are not sufficiently aligned. The central insight for Port Wine brands' managers is that there is a long and continuous work of understanding and associating their descriptors with the most relevant perceptual values and criteria of their targets to reposition (when necessary) and sustainably revitalise their brands. Finally, this study hypothesised a sensory gap that leads to a decrease in consumption, trying to find recommendations on how to transform it into an advantage for a better attraction towards the young age group (18-25).

Keywords: port wine, consumer habits, sensory gap analysis, wine marketing

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Authors: Luciano Tarantino, Emanuele Balzano, Paolo Tarantino, Riccardo Aurelio Nasto, Aurelio Nasto

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Liver transplantation (OLT) is contraindicate in Portal Vein tumor Thrombosis (PVTT) from Hepatocellular Carcinoma at hepatic hilum(pH-HCC) Surgery,Thermal ablation and chemotherapy show poorer outcomes Electrochemotherapy (ECT) has been successfully used in patients with pH-HCC with PVTT. We report the results of ECT as downstaging aimed to definitive cure by OLT. F.P. 53 years HBV related Cirrhosis Child-Pugh B7 class; EGDS F2 aesophageal Varices. Diabetes. April 2016 : Enhanced Computed Tomography (CT) detected HCC(n.3 nodules in VII-VIII-VI;diameter range=25 cm) and PVTT of right portal vein. The patient was considered ineligible for OLT. May 2016: first ablation session with percutaneous Radiofrequency-ablation(RFA) of 3 HCC-nodules . August 2016: second ablation session with ECT of PVTT. CT october 2016: disappearance of PVTT and patent right portal vein. No intraparenchymal recurrence. CT march 2017: No recurrence in portal vein and in the left lobe. local recurrence in the VII-VIII segments. May 2017 : transarterial chemoembolization (TACE) of right lobe recurrences. CT October 2017: patent right portal vein. No recurrence. The patient was reconsidered for OLT. He underwent OLT in April 2018. At 36-months follow-up , no intrahepatic recurrence of HCC occurred. March 2021: enhanced CT and PET/CT detected a single small nodule (1.5 cm) uptaking tracer in the left upper pulmonary lobe, no hepatic recurrence . CT-guided FNB showed metastasis from HCC . June 2021: left lung upper lobectomy . At the current time the patient is alive and recurrence-free at 64 months follow-up. ECT Could be aneffective technique as pre-OLT dowstaging in HCC with PVTT.

Keywords: liver tumor ablation, interventional ultrasound, electrochemotherapy, liver transplantation

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