A Fused Feature Selection Technique for Enhanced Sentiment Analysis Using Deep Learning

Abstract

Abstract Sentiment analysis holds paramount importance in contemporary business landscapes, particularly in leveraging insights from the extensive pool of social media data. The rise of social media platforms, including opinion polls, weblogs, Twitter, and various other networks, has accentuated the need for effective sentiment analysis tools. Deep learning has emerged as a pivotal technique in natural language processing (NLP), particularly for sentiment analysis tasks, owing to its ability to autonomously learn features. However, the performance of deep learning models can suffer when confronted with a large number of features. To address this limitation, this paper proposes a novel fused feature selection technique, Chi-Vec, aimed at selectively passing relevant features to deep learning models. Chi-Vec is a fusion of Chi-square and Word2Vec. The research encompasses the exploration of three distinct datasets; CBET, ATIS, and AWARE. Leveraging the bi-directional Long Short-Term Memory (Bi-LSTM) architecture in conjunction with Chi-Vec, the approach achieves remarkable accuracy rates of 97.96%, 98.41%, and 94.45% for CBET, ATIS, and AWARE dataset respectively. Chi-Vec not only enhances the efficiency and accuracy of sentiment analysis but also demonstrates promising potential for various NLP applications.