Through our project, our group has crawled for the latest customers’ reviews from the various filters and conditions available at the Amazon Customer Reviews webpages via the Google Chrome extension, Amazon Web Scraper. We then created a web interface, consisting of HTML and PHP files, and integrated them with the functionalities of the indexing software, Solr, where the required data manipulation, indexing and selection are carried out. After which, we proceed with data pre-processing using techniques such as merging and formatting the multiple CSV files of crawled data, removal of duplicated data, stemming, lemmatization, stop words removal and count vectorization. Following that, we performed our sentiment prediction using various classification models such as Naïve Bayes classifier, K-nearest Neighbour and Support Vector Machine. The models are then evaluated using evaluation metrics such as F-measure, precision and recall values and the results are compiled and analyzed. The powerful LDAvis library is used to visualize text data without difficulty. Lastly, we explored some innovations to enhance our classification such the use of GridSearch and k-fold Cross Validation and the use of ensemble classification model like Random Forest to compare with the models’ results.