Abstract
The classification of reviews or comments provided by the customers after shopping has a wide scope in terms of the categories it can be classified. Big companies like Walmart, Tesco and Amazon have customers from all over the world with a variety of product range and can have reviews written in any language. Sometimes customers intend to provide reviews not only on the same platform but on various other platforms like Facebook, Twitter. To get an overall picture of the products it’s required to check the reviews from all these platforms at a single place. This paper classifies the comments\reviews written in Spanish language and category names are taken in English language for 30 product categories. The purpose is to get the product categorized from comments/reviews on different platforms in non-English language, to gather insights of that product and to reduce the dependency faced during the manual process of classification and barrier to have command on that language. The approach used reduces the chances of manual errors during prediction of new reviews/comments to a particular category. A multiclass Classification model is trained using traditional Machine Learning algorithms & NLP with an accuracy of 90%. It is envisioned that the proposed methodology is scalable for other non-English languages as well.
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Sharma, P., Parwekar, P. (2023). Multiclass Classification of Online Reviews Using NLP & Machine Learning for Non-english Language. In: Zaynidinov, H., Singh, M., Tiwary, U.S., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science, vol 13741. Springer, Cham. https://doi.org/10.1007/978-3-031-27199-1_9
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