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Sentiment Analysis of Amazon Food Review Data

Sentiment classification is a functioning examination area in information mining and information disclosure with different application spaces. Accomplishment of item advancement sites, for example, Amazon, eBay and so forth gets influenced by the nature of the surveys they have for their yields. Every one of these destinations gives a path to the analyst to form the remarks on the basis of the items and assign a remark to it. Considering these remarks, the analysis will be classified as best or worst. By this, a structure can be edified that can identify the sentiment masked in a review, performing sentiment categorization on a gigantic dataset. All particulars can be grouped into primarily two classes, facts and opinions. Facts are assertions about matter and worldly occurrences. And opinions are individual statements that mirror individuals’ assumptions or bits of knowledge about the entities and events. This paper shows the performance of classification algorithms such as Decision Tree, Bernoulli NB, Logistic, and Perceptron using Principal Component Analysis (PCA), applying n-gram (unigram, bigram) on the entire feature set and computing confusion matrix for the dataset.