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TRABSA: Interpretable Sentiment Analysis of Tweets using Attention-based BiLSTM and Twitter-RoBERTa
April 2, 2024, 7:43 p.m. | Md Abrar Jahin, Md Sakib Hossain Shovon, M. F. Mridha
cs.LG updates on arXiv.org arxiv.org
Abstract: Sentiment analysis is crucial for understanding public opinion and consumer behavior. Existing models face challenges with linguistic diversity, generalizability, and explainability. We propose TRABSA, a hybrid framework integrating transformer-based architectures, attention mechanisms, and BiLSTM networks to address this. Leveraging RoBERTa-trained on 124M tweets, we bridge gaps in sentiment analysis benchmarks, ensuring state-of-the-art accuracy. Augmenting datasets with tweets from 32 countries and US states, we compare six word-embedding techniques and three lexicon-based labeling techniques, selecting the …
abstract analysis architectures arxiv attention attention mechanisms behavior challenges consumer cs.cl cs.lg diversity explainability face framework hybrid networks opinion public roberta sentiment sentiment analysis transformer tweets twitter type understanding
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