Emotion and sentiment analysis from Twitter text
By Kashfia Sailunaz, R. Alhajj. Journal of Computer Science, 2019
358 citations recorded by the source corpus.
Abstract Online social networks have emerged as new platform that provide an arena for people to share their views and perspectives on different issues and subjects with their friends, family, relatives, etc. We can share our thoughts, mental state, moments, stand on specific social, national, international issues through text, photos, audio and video messages and posts. Indeed, despite the availability of other forms of communication, text is still one of the most common ways of communication in a social network. The target of the work described in this paper is to detect and analyze sentiment and emotion expressed by people from text in their twitter posts and use them for generating recommendations. We collected tweets and replies on few specific topics and created a dataset with text, user, emotion, sentiment information, etc. We used the dataset to detect sentiment and emotion from tweets and their replies and measured the influence scores of users based on various user-based and tweet-based parameters. Finally, we used the latter information to generate generalized and personalized recommendations for users based on their twitter activity. The method we used in this paper includes some interesting novelties such as, (i) including replies to tweets in the dataset and measurements, (ii) introducing agreement score, sentiment score and emotion score of replies in influence score calculation, (iii) generating general and personalized recommendation containing list of users who agreed on the same topic and expressed similar emotions and sentiments towards that particular topic.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/2b55f8b31650bb62651af149445ccf39c53a8ce5
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In Artificial Intelligence
Citations in the corpus
- VAuLT: Augmenting the Vision-and-Language Transformer with the Propagation of Deep Language Representations (2022, background): “However, there are situations where images are treated as nuisance during the data collection phase and are readily filtered out e.g., Sailunaz and Alhajj (2019).”