Abstract
In this paper, we have done sentiment analysis for English written Bengali words given in different online shops in Bangladesh. For this work, we have chosen four latest mobile phones popular in Bangladesh. Here, the user reviews were in Bengali words written by English characters. The data was taken from online shopping sites from Bangladesh. Here, we have assumed six different features of mobiles written in the Result section. The main objective of the study was to find out the sentiment of Bengali words written with English alphabets. As it is a trend to write such reviews in Bangladesh, the data was taken and preprocessed to fit in algorithm, and they were compared whether it is positive or negative. Python was used as simulation tool, and Pursehub was used to extract the data set, and the system successfully finds out the positivity and negativity of the reviews. This result was achieved by using confusion matrix and that is making the overall performance of those mobile handsets. Out of 1201 reviews, 599 were found to be negative and 826 were found to be positive. The F1 score was 85.25%, accuracy was achieved 85.31%, and recall rate was 84.95%.
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Ahmed, S.S. et al. (2020). Opinion Mining of Bengali Review Written with English Character Using Machine Learning Approaches. In: Bindhu, V., Chen, J., Tavares, J. (eds) International Conference on Communication, Computing and Electronics Systems. Lecture Notes in Electrical Engineering, vol 637. Springer, Singapore. https://doi.org/10.1007/978-981-15-2612-1_5
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DOI: https://doi.org/10.1007/978-981-15-2612-1_5
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