Zishan Ahmed

Work place: Department of Computer Science, American International University Bangladesh, Kuratoli, Khilkhet, Dhaka 1229, Bangladesh

E-mail: zishanahmed599@gmail.com


Research Interests: Artificial Intelligence and Applications, Data Structures and Algorithms


Zishan Ahmed an enthusiastic undergraduate pursuing a Bachelor of Science in Computer Science and Engineering. He is captivated by the potential of data to alter the world we live in. In data science, natural language processing (NLP), and machine learning, he sees the greatest potential for innovation and influence. His knack for mathematics and programming has been refined throughout his academic career. He is well-versed in several programming languages, including Python, Java, and C++, and is always keen to acquire new tools and technologies. His education has included data structures and algorithms, database management, artificial intelligence, and computer vision.

Author Articles
Binary vs. Multiclass Sentiment Classification for Bangla E-commerce Product Reviews: A Comparative Analysis of Machine Learning Models

By Shakib Sadat Shanto Zishan Ahmed Nisma Hossain Auditi Roy Akinul Islam Jony

DOI: https://doi.org/10.5815/ijieeb.2023.06.04, Pub. Date: 8 Dec. 2023

Sentiment analysis, the process of determining the emotional tone of a text, is essential for comprehending user opinions and preferences. Unfortunately, the majority of research on sentiment analysis has focused on reviews written in English, leaving a void in the study of reviews written in other languages. This research focuses on the understudied topic of sentiment analysis of Bangla-language product reviews. The objective of this study is to compare the performance of machine learning models for binary and multiclass sentiment classification in the Bangla language in order to gain a deeper understanding of user sentiments regarding e-commerce product reviews. Creating a dataset of approximately one thousand Bangla product reviews from the e-commerce website 'Daraz', we classified sentiments using a variety of machine learning algorithms and natural language processing (NLP) feature extraction techniques such as TF-IDF, Count Vectorizer with N-gram methods. The overall performance of machine learning models for multiclass sentiment classification was lower than binary class sentiment classification. In multiclass sentiment classification, Logistic Regression with bigram count vectorizer achieved the maximum accuracy of 82.64%, while Random Forest with unigram TF-IDF vectorizer achieved the highest accuracy of 94.44%. Our proposed system outperforms previous multiclass sentiment classification techniques by a fine margin.

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