Context-based emotion predictor: a decision- making framework for mobile data
Document Type
Article
Publication Date
6-1-2022
Abstract
The proliferation of big data for web-enabled technologies allows users to publish their views, suggestions, sentiments, emotions, and opinionative content about several real-world entities. These available opinionative texts have greater importance to those who are inquisitive about their desired entities, but it becomes an arduous task to capture such a massive volume of user-generated content. Emotions are an inseparable part of communication, which is articulated in multiple ways and can be used for making better decisions to reshape business strategies. Emotion detection is a subdiscipline at the crossroads of text mining and information retrieval. Context is a common phenomenon in emotions and is inherently hard to capture not only for the machine but even for a human. This study proposes a decision-making framework for efficient emotion detection of mobile reviews. An unsupervised lexicon-based algorithm has been developed to tackle the problem of emotion prediction. Dictionaries and corpora are used as backend resources in the semantic orientation of emotion words, whereas the major contribution is to cope with contextualized emotion detection. The proposed framework outperformed the existing emotion detection systems by achieving 86% accuracy over mobile reviews.
Keywords
Sentiment analysis, Lexicon, Recognition
Divisions
Computer
Funders
Taif University, Taif, Saudi Arabia [Grant No; TURSP-2020/36],Faculty of Computer Science and Information Technology, University of Malaya under Postgraduate Research Grant [Grant No; PG035-2016A]
Publication Title
Mobile Information Systems
Volume
2022
Publisher
IOS Press
Publisher Location
ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND