Year: 2024
Type: article
Abstract: Dedicated Identifying aimed at discerning subjective information by analyzing the polarity of opinions conveyed in text. Traditionally, recurrent neural networks (RNNs) have been the dominant approach for this type of analysis because of their ability to process sequential data. However, sentiment analysis has experienced a significant transformation with the introduction of convolutional neural networks (CNNs). Originally developed for image analysis, processing text due to their efficient mechanisms for extracting local features.. This paper explores the role of CNNs in sentiment analysis, evaluating their architecture, methodology, and comparative effectiveness against RNN-based models. We propose a comprehensive CNN-based model for sentiment analysis and examine its potential for sentiment classification tasks across multiple datasets.
Author: Pankaj Sharma
Institution: Chandigarh University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404224040