• Vaishnavi S. Harale
• Prof. Swati P. Gade
• Shruti M. Gaware
• Mrunal K. Badade
• Mansi S. Lohare

Recently, Sentiment Analysis (SA) has become one of the most interesting topics in text analysis, due to its promising commercial benefits. One of the main issues facing SA is how to extract emotions inside the opinion, and how to detect fake positive reviews and fake negative reviews from opinion reviews. Moreover, the opinion reviews obtained from users can be classified into positive or negative reviews, which can be used by a consumer to select a product. This paper aims to classify product reviews into groups of positive or negative polarity by using machine learning algorithms. In this study, we analyze online product reviews using SA methods in order to detect fake reviews. SA and text classification methods are applied to a dataset of product reviews. More specifically, we compare five supervised machine learning algorithms: Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN- IBK), K-Star (K*) and Decision Tree (DT-J48) for sentiment classification of reviews using two different datasets, including product review dataset V2.0 and product reviews dataset V1.0. The measured results of our experiments show that the SVM algorithm outperforms other algorithms, and that it reaches the highest accuracy not only in text classification, but also in detecting fake reviews.

Sentiment Analysis; Fake Reviews; Naïve Bayes; Support Vector Machine; k-Nearest Neighbor; K-Star; Decision Tree -J48.

"Product Fake Reviews Detection with Sentiment Analysis Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 11, page no.c157-c160, November-2022, Available :http://www.jetir.org/papers/JETIR2211213.pdf

"Product Fake Reviews Detection with Sentiment Analysis Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 11, page no. ppc157-c160, November-2022, Available at : http://www.jetir.org/papers/JETIR2211213.pdf