Browsing by Author "Aas Suhaeel, Abdullah"

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    Sentiment Analysis of Book Review in Bangla Using NLP and Machine Learning
    (Daffodil International University, 2022-01-05) khan, Raihan; Aas Suhaeel, Abdullah
    Sentiment polarity detection has recently piqued the interest of NLP researchers, owing to the resulting in positive of consumer comments or ratings on the internet. Due to the continued expansion of e-commerce sites, the rate of purchase of various products has grown. For example, people's interest in books is fast growing. Online marketing and ecommerce companies were already prospering in Bangladesh during this era of internet technology. For example, product reviews on the Internet have become an essential source of information for customers making purchasing decisions. Because there are sometimes too many reviews for consumers to read, figuring out how to automatically classify and determine sentiment from them has become a major research challenge. Books are said to be a person's best friend. Books are crucial in every human's life because they provide information of the outside world, improve their reading, writing, and speaking abilities, and improve memory and intellect. Even just a few years ago, individuals in Bangladesh had to travel to the library in person to get books. Many of the benefits are straightforward, and the internet bookshop has a disadvantage in that the reader is unfamiliar with the books or with the book store itself. To avoid this, book readers prefer to rely on reviews and ratings. Our goal is to assess Bangladeshi language reviews and give accurate information about books and online bookstores so that book lovers may buy the right books to read and find better online bookstores. In this paper we show how to extract sentiment polarity (positive or negative) from Bengali book reviews using machine learning and Natural Language (NLP).We used five classification methods : Adaboost ,Decision Tree (DT), Random Forest Tree (RFT), Support Vector Machine (SVM), and lightGbm algorithm.