Identifying Fake News using Emotion Analysis

This paper presents research applying Emotional Analysis to “Fake News” and “Real News” articles to investigate whether or not there is a difference in the emotion used in these two types of news articles. The paper reports on a dataset for Fake and Real News that we created, and the natural language processing techniques employed to process the collected text. We use a lexicon that includes predefined words for eight emotions (anger, anticipation, disgust, fear, surprise, sadness, joy, trust) to measure the emotional impact in each of these eight dimensions. The results of the emotion analysis are used as features for machine learning algorithms contained in the Weka package to train a classifier. This classifier is then used to analyze a new document to predict/classify it to be “Fake” or “Real” News

Leveraging the power of transformers for guilt detection in text

In recent years, language models and deep learning techniques have revolutionized natural language processing tasks, including emotion detection. However, the specific emotion of guilt has received limited attention in this field. In this research, we explore the applicability of three transformer-based language models for detecting guilt in text and compare their performance for general emotion detection and guilt detection. Our proposed model outformed BERT and RoBERTa models by two and one points respectively. Additionally, we analyze the challenges in developing accurate guilt-detection models and evaluate our model's effectiveness in detecting related emotions like "shame" through qualitative analysis of results

Emotion Recognition Of Animals Using Natural Language Processing

Sentiment analysis, also known as opinion mining, is a Natural Language Processing (NLP) technique that holds a pivotal role in discerning textual data's sentiments, categorizing them as positive, negative, or neutral. Its significance is underscored by its widespread use in aiding businesses to gauge brand and product sentiment from customer feedback, enhancing customer service, and identifying areas for product and service improvement. Moreover, sentiment analysis offers the ability to track sentiments in real-time, helping companies retain existing customers and attract new ones cost-effectively. Emotion recognition in animals using Natural Language Processing (NLP) is a challenging and less explored area compared to human emotion recognition. While animals do communicate their emotions through various non-verbal cues, such as body language, vocalizations, and facial expressions, applying NLP techniques directly may not be straightforward since animals don't use language in the same way humans do. However, if there are textual data associated with animal behavior, such as ethological observations or written descriptions of their activities, NLP techniques can be adapted to gain insights into their emotional states. &nbsp

Word Affect Intensities

Words often convey affect -- emotions, feelings, and attitudes. Lexicons of word-affect association have applications in automatic emotion analysis and natural language generation. However, existing lexicons indicate only coarse categories of affect association. Here, for the first time, we create an affect intensity lexicon with real-valued scores of association. We use a technique called best-worst scaling that improves annotation consistency and obtains reliable fine-grained scores. The lexicon includes terms common from both general English and terms specific to social media communications. It has close to 6,000 entries for four basic emotions. We will be adding entries for other affect dimensions shortly

Bridging Emotion Role Labeling and Appraisal-based Emotion Analysis

The term emotion analysis in text subsumes various natural language processing tasks which have in common the goal to enable computers to understand emotions. Most popular is emotion classification in which one or multiple emotions are assigned to a predefined textual unit. While such setting is appropriate to identify the reader's or author's emotion, emotion role labeling adds the perspective of mentioned entities and extracts text spans that correspond to the emotion cause. The underlying emotion theories agree on one important point; that an emotion is caused by some internal or external event and comprises several subcomponents, including the subjective feeling and a cognitive evaluation. We therefore argue that emotions and events are related in two ways. (1) Emotions are events; and this perspective is the fundament in NLP for emotion role labeling. (2) Emotions are caused by events; a perspective that is made explicit with research how to incorporate psychological appraisal theories in NLP models to interpret events. These two research directions, role labeling and (event-focused) emotion classification, have by and large been tackled separately. We contributed to both directions with the projects SEAT (Structured Multi-Domain Emotion Analysis from Text) and CEAT (Computational Event Evaluation based on Appraisal Theories for Emotion Analysis), both funded by the German Research Foundation. In this paper, we consolidate the findings and point out open research questions.Comment: under review for https://bigpictureworkshop.com

Verba Emosi Statif dalam Bahasa Melayu Asahan

This research proposes a new perspective in analyzing emotion stative verbs, i.e. starting from meaning to form, by presenting the evidence from the Asahan Malay language. The data was collected by using questionnaire, observation, interview, and intuition methods. The analysis concerns the mapping of semantic components of emotion stative verbs, which is used to determine their subcategory. For the analysis, the semantic primes of the Natural Semantic Metalanguage theory are applied. The study shows that emotion stative verbs of Asahan Malay are characterized by the component ‘X felt something not because X wanted this'. In accordance with the types of events, emotion stative verbs are divided into four subcategories: (1) ‘something bad has happened' (“sodih-like”), (2) ‘something bad can/will happen' (“takut-like”), (3) ‘people can know something bad about me' (“malu-like”), and (4) ‘I don't think that things like this can/will happen' (“heran-like”)