How are you doing? : emotions and personality in Facebook

User generated content on social media sites is a rich source of information about latent variables of their users. Proper mining of this content provides a shortcut to emotion and personality detection of users without filling out questionnaires. This in turn increases the application potential of personalized services that rely on the knowledge of such latent variables. In this paper we contribute to this emerging domain by studying the relation between emotions expressed in approximately 1 million Facebook (FB) status updates and the users' age, gender and personality. Additionally, we investigate the relations between emotion expression and the time when the status updates were posted. In particular, we find that female users are more emotional in their status posts than male users. In addition, we find a relation between age and sharing of emotions. Older FB users share their feelings more often than young users. In terms of seasons, people post about emotions less frequently in summer. On the other hand, December is a time when people are more likely to share their positive feelings with their friends. We also examine the relation between users' personality and their posts. We find that users who have an open personality express their emotions more frequently, while neurotic users are more reserved to share their feelings

Identifying Emotions in Social Media: Comparison of Word-emotion lexica

In recent years, emotions expressed in social media messages have become a vivid research topic due to their influence on the spread of misinformation and online radicalization over online social networks. Thus, it is important to correctly identify emotions in order to make inferences from social media messages. In this paper, we report on the performance of three publicly available word-emotion lexicons (NRC, DepecheMood, EmoSenticNet) over a set of Facebook and Twitter messages. To this end, we designed and implemented an algorithm that applies natural language processing (NLP) techniques along with a number of heuristics that reflect the way humans naturally assess emotions in written texts. In order to evaluate the appropriateness of the obtained emotion scores, we conducted a questionnaire-based survey with human raters. Our results show that there are noticeable differences between the performance of the lexicons as well as with respect to emotion scores the human raters provided in our surve

The application of sentiment analysis to a psychotherapy session : an exploratory study using four general-purpose lexicons

Dissertação de Mestrado apresentada no ISPA – Instituto Universitário para obtenção de grau de Mestre na especialidade de Psicologia Clínica.In this study we explore the application of sentiment analysis to a complete and in-person psychotherapy session. Sentiment analysis is a text mining technique that allows for the analysis, interpretation, and visualization of textual data. We investigate how we can apply a lexicon-based approach to analyze clinical session data, using four general-purpose lexicons available within an open-source statistical programming language environment, R. We conducted our study by comparing the performance of four general-purpose lexicons to the performance of n = 52 human raters, using inter-rater reliability (IRR) and intraclass correlation (ICC) measurements. Our findings suggest there is low to moderate agreement between human ratings and lexicon generated ratings, depending on the lexicon used. There are some benefits in applying a lexicon-based sentiment analysis approach to psychotherapy session data, namely the way it efficiently processes and analyses data and allows for novel visualizations of psychotherapy data. We recommend further investigation into the application of sentiment analysis as a technique, focusing on the performance of specific-purpose lexicons. We also recommend further research into comparing the performance of lexicon-based approaches to text classification approaches to the analysis of psychotherapy data

English Studies and Literary Education in the Era of Media Manipulation: Context, Perceptions, Feelings and Challenges

This article analyses the components of a method of literary education aimed at strengthening critical awareness. It discusses whether the current academic context is hospitable to a literary education that fights against the over-simplification of our epistemological horizons. The popularisation of a utilitarian version of university study, the neglect of reflective practices and the marginalisation of the usefulness of the discipline of literature within the field of English Studies are some of the realities that we currently face. Within this context, a literary education involving activism can play an important role in promoting resistance against the pandemic of media manipulation we are in the midst of. After having examined the views of a group of students at the University of Jaén (Spain) concerning the importance of studying an English Studies degree in contemporary society, it is clear that such an education needs to be based on emotional aspects, paying special attention to the students’ feelings and perceptions. The results of our corpus-based study using Sentiment Analysis techniques evidence the emotional disaffection of students from certain subjects, namely literature, which are specifically aimed at encouraging critical thinking. Thus, one of the future challenges that must be faced is to foster positive emotions in our literature lessons, as they are essential to promote the students’ critical awareness and activism

Advancement of artificial intelligence techniques based lexicon emotion analysis for vaccine of COVID-19

Emotions are a vital and fundamental part of life. Everything we do, say, or do not say, somehow reflects some of our feelings, perhaps not immediately. To analyze a human's most fundamental behavior, we must examine these feelings using emotional data, also known as affect data. Text, voice, and other types of data can be used. Affective Computing, which uses this emotional data to analyze emotions, is a scientific fields. Emotion computation is a difficult task; significant progress has been made, but there is still scope for improvement. With the introduction of social networking sites, it is now possible to connect with people from all over the world. Many people are attracted to examining the text available on these various social websites. Analyzing this data through the Internet means we're exploring the entire continent, taking in all of the communities and cultures along the way. This paper analyze text emotion of Iraqi people about COVID-19 using data collected from twitter, People's opinions can be classified based on lexicon into different separate classifications of feelings (anticipation, anger, trust, fear, sadness, surprise, disgust, and joy) as well as two distinct emotions (positive and negative), which can then be visualized using charts to find the most prevalent emotion using lexicon-based analysis