Abstract
The amount of textual data generation has increased enormously due to the effortless access of the Internet and the evolution of various web 2.0 applications. These textual data productions resulted because of, the people express their opinions, emotions or sentiment regarding any service or product through tweets, posts, and reviews. Sentiment analysis concerns the computational technique of determining and classifying views stated in a piece of text, primarily to identify whether the author’s attitude toward a specific issue is positive, negative, or neutral. The impact of customer review is significant to perceive the customer attitude towards a restaurant. Thus, the automatic detection of sentiment from reviews is advantageous for the restaurant owners, service providers as well as customers to make their decisions or services more satisfactory. This paper proposes, a deep learning-based technique (i.e., BiLSTM) to classify the restaurant reviews expressed in Bengali into positive and negative polarities. A corpus consists of 8435 reviews is constructed to evaluate the proposed technique. The results of the evaluation on test dataset shows that BiLSTM technique provides the highest accuracy of 91.35% compared to other existing techniques.
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Hossain, E., Sharif, O., Hoque, M.M., Sarker, I.H. (2021). SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant Reviews. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_19
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