Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3454 2018 2018 Department of Information Technology Indira Gandhi Delhi Technical University for Women, New Delhi 110006, India Oday Oday Computer Science and Information Technology, University of Wasit, Al Kut 52001, Iraq Huda Lafta Majeed University of Information Technology and Communication, Iraq Hassan Muayad Ibrahim Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi 110058, India Nishtha Jatana Department of Computer Science and Engineering, Bhagwan Parshuram Institute of Technology, Delhi-85, India Charu Gupta Guru Gobind Singh Indraprastha University, Delhi, India Agam Kumar Guru Gobind Singh Indraprastha University, Delhi, India Bharti Suri Ministry of Education, Wasit Education Directorate, Kut 52001, Iraq Oday Ali Hassen The world is witnessing a boom in the digital age. Digital shops have literally landed into our homes. Almost any required product can now be purchased online via websites or mobile apps without having to step out. Due to online shopping, many customers rely on online reviews from other customers before making a purchase. Customer reviews are gaining more and more importance as they play a probably vital role in the sale and purchase of a product. Customer reviews also provide firsthand feedback coming directly from the customers themselves; this can benefit even the sellers in improving future sales. Analyzing the reviews can provide probable causes for failure or success of a product. Henceforth, the current paper presents the sentiment analysis of the reviews to better understand the feelings expressed by the customers. The very popular and widely used mobile phones were chosen as the product and Amazon was chosen as the digital seller for the current study. Initially, this work began with data preprocessing. Followed by data preprocessing, Bow and n-grams word embedding have been used to represent the clean reviews in vector representation, and then the features were derived. Finally, the performance of supervised machine learning classifiers such as Decision Tree, Naive Bayes, Random Forest, and SVM was empirically evaluated through accuracy, recall, f1-score, and precision. The results of empirical evaluation revealed that the Random Forest Classifier shows best performance with 97.48% accuracy. 2025 2025 116 129 10.54216/FPA.180110 https://www.americaspg.com/articleinfo/3/show/3454