Bidirectional Recurrent Capsule Networks with Transformer Based Learning for Twitter Sentiment Classification
Keywords:
Bidirectional Recurrent Capsule Networks, Sentiment Analysis, Transformer, Capsule Network, BiLSTM, GRU, Self-attention, Deep Learning
Abstract
Rapid developments have been happening in recent years in terms of classifying aspect-level sentiment analysis (ASA). Several research gaps have been identified with the existing approaches like aspect extraction, opinion target aspects, and handling long-term dependencies between the contextual aspects. In this work, we focus on addressing the above limitations by proposing Bidirectional Recurrent Capsule Networks (BRCN) framework with a pre-trained BERTweet transformer and sequence-based self-attention mechanism. This combination relatively improves the task of ASA. Furthermore, we also contributed to the development of one dataset on Covid-19 based on public tweets collected from social media to analyze its intensity. Based on the experimental results, it is evident that the proposed model surpasses other traditional approaches by identifying the hidden contextual information using sequence position information. In addition, we evaluated the performance of our proposed model against other approaches using three benchmark datasets, namely IMDB, HNT, and MR. Our findings reveal that our model achieved higher accuracy and had better time complexity than the existing approaches.
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Published
2024-11-22
How to Cite
Simran Rawat Gaurav Suratwala, Aman Singh Jagat Jeet Biswas, Jeffrey Obiri Boahen Hanumant Jagdish Chauhan, Mudit Parihar, Yashwardhan Atre, Bhavani Sangani, & Prof. Hardik Parmar. (2024). Bidirectional Recurrent Capsule Networks with Transformer Based Learning for Twitter Sentiment Classification. Revista Electronica De Veterinaria, 25(2), 1666-1684. https://doi.org/10.69980/redvet.v25i2.1938
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