Year: 2025
Type: article
Abstract: : Although significant advancements have been made in tweet sentiment analysis—particularly with the rise of deep learning and the availability of large, diverse training datasets—research in this area has often remained focused on traditional forms of content such as movie and product reviews. While recent datasets extend beyond basic emoticons and hashtags, many earlier sentiment analysis studies have primarily addressed tweets with only two sentiment polarities: positive and negative. Moreover, these systems frequently fail to align sentiment classifications with specific target entities or topics. In this paper, we explore several deep learning techniques for sentiment analysis on Twitter data. We also trained models using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), achieving promising accuracy in sentiment classification.
Authors: Bhumika Sablok, Ankush Tyagi, Dharmander, Mr. Harendra Singh Malik, Dr. Saumya Chaturvedi, Dr. Sureshwati
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409599349