Improvement of feature engineering on emotion detection from textual data
School of Education Technology, Jadavpur University, Kolkata, India.
Review
World Journal of Advanced Engineering Technology and Sciences, 2024, 12(01), 073–076.
Article DOI: 10.30574/wjaets.2024.12.1.0171
Publication history:
Received on 25 March 2024; revised on 06 May 2024; accepted on 09 May 2024
Abstract:
This paper introduces a new method for selecting terms in the field of emotion recognition from text. Instead of focusing solely on very common or very rare terms, this approach considers moderately frequent terms as well. The idea is that these moderately frequent terms might also contain important information for distinguishing between emotions. Compared to traditional methods like Chi-Square and Gini-Text, this new approach performs better in many cases. To represent documents, the bag-of-words approach is used, where each document is represented by a vector. In this vector, each selected term is given a weight of 1 if it appears in the document, and 0 if it does not. Importantly, this new method includes terms that are not selected by Chi-Square and Gini-Text. Experiments conducted on a standard dataset demonstrate that including moderately frequent terms improves the accuracy of emotion recognition. This improvement is evident in terms of accuracy scores.
Keywords:
Text categorization; Emotion Detection; Feature selection; Machine learning
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