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Attention-based convolutional neural network for Bangla sentiment analysis

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Abstract

With the accelerated evolution of the internet in the form of web-sites, social networks, microblogs, and online portals, a large number of reviews, opinions, recommendations, ratings, and feedback are generated by writers or users. This user-generated sentiment content can be about books, people, hotels, products, research, events, etc. These sentiments become very beneficial for businesses, governments, and individuals. While this content is meant to be useful, a bulk of this writer-generated content requires using text mining techniques and sentiment analysis. However, there are several challenges facing the sentiment analysis and evaluation process. These challenges become obstacles in analyzing the accurate meaning of sentiments and detecting suitable sentiment polarity specifically in the Bangla language. Sentiment analysis is the practice of applying natural language processing and text analysis techniques to identify and extract subjective information from text. This paper presents how the attention mechanism could be incorporated effectively and efficiently in analyzing the Bangla sentiment or opinion.

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  1. Department of CSE, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

    Sadia Sharmin & Danial Chakma

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  1. Sadia Sharmin
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  2. Danial Chakma
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Correspondence to Sadia Sharmin.

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Sharmin, S., Chakma, D. Attention-based convolutional neural network for Bangla sentiment analysis. AI & Soc 36, 381–396 (2021). https://doi.org/10.1007/s00146-020-01011-0

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