Analysis of Information Spreading by Social Media Based on Emotion and Empathy

The number of social media users has increased exponentially in recent times, and various types of social media platforms are being introduced. While social media has become a convenient communication tool, its use has caused various social problems. Some users who cannot imagine the emotions their posts may induce in readers cause what is termed as “the flaming phenomenon.” In some cases, users intentionally repeat strong remarks for self-advertisement. To identify the cause of this phenomenon, it is necessary to analyze the posted contents or the personalities of the users who cause the flaming. However, it is difficult to reach a generalized conclusion because each case varies depending on the circumstances and individual. In this chapter, we study the phenomenon of information spreading via communication on social media by conducting a detailed analysis of replies and number of retweets in Japanese, and we reveal the relation between the feedback on such posts and the emotions or empathy they result in

Techniques for improving the labelling process of sentiment analysis in the Saudi stock market

Sentiment analysis is utilised to assess users' feedback and comments. Recently, researchers have shown an increased interest in this topic due to the spread and expansion of social networks. Users' feedback and comments are written in unstructured formats, usually with informal language, which presents challenges for sentiment analysis. For the Arabic language, further challenges exist due to the complexity of the language and no sentiment lexicon is available. Therefore, labelling carried out by hand can lead to mislabelling and misclassification. Consequently, inaccurate classification creates the need to construct a relabelling process for Arabic documents to remove noise in labelling. The aim of this study is to improve the labelling process of the sentiment analysis. Two approaches were utilised. First, a neutral class was added to create a framework of reliable Twitter tweets with positive, negative, or neutral sentiments. The second approach was improving the labelling process by relabelling. In this study, the relabelling process applied to only seven random features (positive or negative): "earnings" (Arabic source), "losses" (Arabic source), "green colour" (Arabic source:Arabic source), "growing" (Arabic source), "distribution" (Arabic source), "decrease" (Arabic source), "financial penalty" (Arabic source), and "delay" (Arabic source). Of the 48 tweets documented and examined, 20 tweets were relabelled and the classification error was reduced by 1.34%

Relationship Between Personality Patterns and Harmfulness : Analysis and Prediction Based on Sentence Embedding

This paper hypothesizes that harmful utterances need to be judged in the context of whole sentences, and the authors extract features of harmful expressions using a general-purpose language model. Based on the extracted features, the authors propose a method to predict the presence or absence of harmful categories. In addition, the authors believe that it is possible to analyze users who incite others by combining this method with research on analyzing the personality of the speaker from statements on social networking sites. The results confirmed that the proposed method can judge the possibility of harmful comments with higher accuracy than simple dictionary-based models or models using a distributed representation of words. The relationship between personality patterns and harmful expressions was also confirmed by an analysis based on a harmful judgment model

New techniques and framework for sentiment analysis and tuning of CRM structure in the context of Arabic language

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyKnowing customers’ opinions regarding services received has always been important for businesses. It has been acknowledged that both Customer Experience Management (CEM) and Customer Relationship Management (CRM) can help companies take informed decisions to improve their performance in the decision-making process. However, real-word applications are not so straightforward. A company may face hard decisions over the differences between the opinions predicted by CRM and actual opinions collected in CEM via social media platforms. Until recently, how to integrate the unstructured feedback from CEM directly into CRM, especially for the Arabic language, was still an open question. Furthermore, an accurate labelling of unstructured feedback is essential for the quality of CEM. Finally, CRM needs to be tuned and revised based on the feedback from social media to realise its full potential. However, the tuning mechanism for CEM of different levels has not yet been clarified. Facing these challenges, in this thesis, key techniques and a framework are presented to integrate Arabic sentiment analysis into CRM. First, as text pre-processing and classification are considered crucial to sentiment classification, an investigation is carried out to find the optimal techniques for the pre-processing and classification of Arabic sentiment analysis. Recommendations for using sentiment analysis classification in MSA as well as Saudi dialects are proposed. Second, to deal with the complexities of the Arabic language and to help operators identify possible conflicts in their original labelling, this study proposes techniques to improve the labelling process of Arabic sentiment analysis with the introduction of neural classes and relabelling. Finally, a framework for adjusting CRM via CEM for both the structure of the CRM system (on the sentence level) and the inaccuracy of the criteria or weights employed in the CRM system (on the aspect level) are proposed. To ensure the robustness and the repeatability of the proposed techniques and framework, the results of the study are further validated with real-word applications from different domains

Off and Online Journalism and Corruption

This book provides a new theoretical framework of determinants that interact together in five hierarchical levels to restrain or produce corruption. The theory suggests a multilevel analysis that tests hypotheses regarding the relations of journalism and corruption within each level and across levels in international comparative research designs. Corruption as the abuse of power for private gain is built into the journalistic, economic, political, and cultural structures of any society and is affected by its interaction within the international system. The important questions of how differences in corruption across countries can be explained or what makes it more or less in a particular society and how press freedom and social media contribute to the fight against corruption are still unanswered. This book represents a significant contribution on the way to answer these critical questions. It discusses a variety of journalism-corruption experiences that provide a wealth of results and analyses. The cases it examines extend from Cuba to Algeria, India, Saudi Arabia, Sub-Saharan African, Gulf Cooperation Countries, Arab World, and Japan. The primary contribution of this book is both theoretical and empirical. Its details as well as the general theoretical frameworks make it a useful book for scholars, academics, undergraduate and graduate students, journalists, and policy makers