Enhancing Emotion Recognition in Text Data Based on Bi-LSTM and Attention Approach

DOI

10.2991/978-94-6463-540-9_84How to use a DOI?

Keywords

Emotion Recognition; Bi-LSTM; Attention Mechanism; Sentiment Analysis

Abstract

Emotion recognition stands as a cornerstone across various domains, propelling the evolution of artificial intelligence. This paper introduces a pioneering approach to emotion recognition, employing a Bi-directional Long Short-Term Memory (Bi-LSTM) neural network fused with an attention mechanism (Att). The primary aim is to enrich the representation of emotional features within text data, particularly for sentiment analysis endeavors. The Bi-LSTM network effectively captures bidirectional dependencies within text sequences, while the Att meticulously focuses on pivotal segments of the input text, thereby enhancing performance and accuracy. Through experimentation on the Sentiment140 dataset, the model’s efficacy is demonstrated, showcasing heightened accuracy and adaptability in contrast to conventional methods. The fusion of Bi-LSTM with Att presents a promising pathway for advancing sentiment analysis tasks, offering valuable insights into the intricacies of emotion recognition within text data. The outcomes of this research not only hold significance for social media analysis and intelligent customer service but also pave the way for potential applications in medical domains such as emotional health monitoring and mental illness diagnosis. Thus, fostering the application of artificial intelligence in diverse fields.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zhuojun Lyu
PY  - 2024
DA  - 2024/10/16
TI  - Enhancing Emotion Recognition in Text Data Based on Bi-LSTM and Attention Approach
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 842
EP  - 851
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_84
DO  - 10.2991/978-94-6463-540-9_84
ID  - Lyu2024
ER  -