Bidirectional Long-Short-Term Memory with Attention Mechanism for Emotion Analysis in Textual Content

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.

@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},
  volume    = {14},
  number    = {6},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Batyrkhan Omarov and Zhandos Zhumanov},
  doi       = {10.14569/IJACSA.2023.0140615},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140615}
}