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

Omarov B. Zhumanov Z.
2023 Science and Information Organization

International Journal of Advanced Computer Science and Applications
2023 #14 Issue 6 129 - 136 pp.

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 models 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.

artificial intelligence , BiLSTM , classification , Deep learning , emotion detection , machine learning

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Suleyman Demirel University, Kaskelen, Kazakhstan
Alem Research, Almaty, Kazakhstan

Suleyman Demirel University
Alem Research

10 лет помогаем публиковать статьи Международный издатель

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