A LIGHTWEIGHT LSTM FRAMEWORK FOR CONTEXTUAL SENTIMENT CLASSIFICATION

With the rapid increase in opinion-rich content shared across the internet, text sentiment analysis has emerged as a vital tool in both academic research and industrial applications. Sentiment analysis typically involves classifying a piece of text as expressing positive, negative, or neutral emotion. Traditional approaches to text classification often require extensive feature engineering and rely heavily on tokenization and embedding techniques, making them resource-intensive and less adaptive to context. To address these limitations, Long Short-Term Memory (LSTM) networks—an advanced form of Recurrent Neural Networks (RNNs)—have been adopted for their ability to capture long-range dependencies in textual data. This study proposes a sentiment classification model based solely on LSTM architecture to analyze short texts and effectively extract context-aware sentiment patterns. Unlike conventional models, LSTM-based frameworks can learn temporal word relationships without explicit syntactic parsing or handcrafted features. By leveraging the memory capabilities of LSTM, the proposed model enhances sentiment categorization accuracy while maintaining a relatively lightweight computational profile. Experimental evaluations demonstrate the effectiveness of LSTM in capturing contextual semantics, making it a suitable choice for real-time sentiment detection tasks in dynamic and user-generated content environments.Received 25 May 2024Accepted 12 June 2024 Published 30 June 2024 DOI10.29121/granthaalayah.v12.i6.2024.6095Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.Copyright: © 2024 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License. With the license CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.1.INTRODUCTIONIn today’s data-driven world, the volume of digital content is growing at an exponential pace. With this surge in online content, especially in the form of user-generated opinionated texts, accessing relevant information has become increasingly challenging. Text classification offers a promising solution to this problem by enabling the automatic identification and categorization of sentiments embedded in textual data. The popularity of sentiment analysis has witnessed a sharp rise not only in academic research but also across commercial platforms like Amazon, Flipkart, Myntra, Ajio, and JioMart, where understanding customer feedback in real time holds significant value [1].