Context-Based Emotion Predictor: A Decision- Making Framework for Mobile Data

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.

Impact of tweets on Indian movies performance: An integrative approach using sentiment analysis and emotion detection

“…ML systems perform numerous tasks, including part-of-speech (POS) labeling, lemmatization, stemming, co-reference, spam detection, word sense disambiguation (WSD) parsing, and detection of dataset popularity. SA's primary objective is to identify public sentiment toward targeted entities (Anwar, 2022).…”