Reductive Bias with Gated Recurrent Sentiment Analysis for Social Media Texts Using the Twitter Dataset

D. Srilatha; Harikrisha Bommala

doi:10.2478/ijssis-2026-0015

Abstract

Recently, people are progressively expressing their feelings and opinions on social media sites such as Facebook, twitter, and Instagram, and interacting with other people worldwide. However, the classification of customer opinions is challenging due to the lack of relevant information about categorizing them into specific classes. To solve this problem, a reductive bias-based gated recurrent unit (RD-GRU) approach is proposed to enhance the classification of sentiments in the Twitter dataset effectively. Initially, the data from Sentiment140 and Twitter API are preprocessed by four different techniques, then the features are extracted by Log term frequency (LTF) and modified inverse class frequency (MICF) technique. The features are selected based on principal component object (PCO) and the proposed IBAO algorithm efficiently. Finally, classification of user review is performed by the SA-Bi-LSTM approach that categorizes the reviews into Positive, Negative, and Neutral classes. The results of the proposed model for sentiment analysis (SA) is evaluated by the performance metrics which attained accuracy of 98.92% and 97.84% for Sentiment140 dataset and Twitter API datasets is superior than existing models such as convolutional neural network (CNN) and long short-term memory (LSTM) approaches.

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Reductive Bias with Gated Recurrent Sentiment Analysis for Social Media Texts Using the Twitter Dataset

Abstract

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