Abstract: Emotion analysis in textual content plays a crucial role in various applications, including sentiment analysis, customer feedback monitoring, and mental health assessment. Traditional machine learning and deep learning techniques have been employed to analyze emotions; however, these methods often fail to capture complex and long-range dependencies in text. To overcome these limitations, this paper proposes a novel bidirectional long-short-term memory (Bi-LSTM) model for emotion analysis in textual content. The proposed Bi-LSTM model leverages the power of recurrent neural networks (RNNs) to capture both the past and future context of text, providing a more comprehensive understanding of the emotional content. By integrating the forward and backward LSTM layers, the model effectively learns the semantic representations of words and their dependencies in a sentence. Additionally, we introduce an attention mechanism to weigh the importance of different words in the sentence, further improving the model's interpretability and performance. To evaluate the effectiveness of our Bi-LSTM model, we conduct extensive experiments on Kaggle Emotion detection dataset. The results demonstrate that our proposed model outperforms several state-of-the-art baseline methods, including traditional machine learning algorithms, such as support vector machines and naive Bayes, as well as other deep learning approaches, like CNNs and vanilla LSTMs.

Batyrkhan Omarov and Zhandos Zhumanov. “Bidirectional Long-Short-Term Memory with Attention Mechanism for Emotion Analysis in Textual Content”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.6 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140615

@article{Omarov2023,
title = {Bidirectional Long-Short-Term Memory with Attention Mechanism for Emotion Analysis in Textual Content},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140615},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140615},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {6},
author = {Batyrkhan Omarov and Zhandos Zhumanov}
}