In this study, I conducted sentiment analysis on product reviews of unlocked mobile phones sold on Amazon to explore customer’s opinions and sentiments towards these devices. I classified the sentiment according to the given rating by user and according to the written reviews by the users respectively. This study collected a total of 400000 reviews from the Amazon website, focusing on unlocked mobile phones from various brands. The reviews were pre-processed and analyzed using Natural Language Processing (NLP) techniques, Bag of Words (BoW) model, LinearSVC, Word2Vec model and Long Short-Term Memory (LSTM) neural network. My analysis revealed that the majority of the reviews (approximately 70%) were positive. The positive reviews highlighted features such as the device's camera quality, battery life, display, and user interface. On the other hand, some negative reviews were found, mainly related to issues with the device's software and hardware. The negative reviews highlighted problems such as slow performance, freezing, and device malfunctioning.Moreover, the study found that some ratings does not corresponds to actual sentiment of review. Some users gave ratings higher or lower compared to the calculated sentiment of then reviews.