A ConvLSTM model with word-level attention for sentiment analysis of review data

Shengnan Wang; Fei Sun; Pingshan Liu

doi:10.1117/12.3049682

8 November 2024 A ConvLSTM model with word-level attention for sentiment analysis of review data

Shengnan Wang, Fei Sun, Pingshan Liu

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134162X (2024) https://doi.org/10.1117/12.3049682
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Deep learning models hold promise for sentiment analysis (SA), traditional CNN and LSTM methods are limited in capturing both local and temporal features, affecting accuracy. This paper introduces a ConvLSTM model with word-level attention to address these issues. By combining CNNs and LSTMs with an attention mechanism, the model improves sentiment classification by focusing on relevant features. Experiments on the IMDB, SD4A, and Sentimen140 datasets show that the proposed model outperforms baseline methods in precision.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shengnan Wang, Fei Sun, and Pingshan Liu "A ConvLSTM model with word-level attention for sentiment analysis of review data", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134162X (8 November 2024); https://doi.org/10.1117/12.3049682

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