The Sentiment Analysis Review

1500 Words6 Pages
Abstract— Sentiment analysis is the computational study of opinions, sentiments, subjectivity, evaluations, attitudes, views and emotions expressed in text. Sentiment analysis is mainly used to classify the reviews as positive or negative or neutral with respect to a query term. This is useful for consumers who want to analyse the sentiment of products before purchase, or viewers who want to know the public sentiment about a new released movie. Here I present the results of machine learning algorithms for classifying the sentiment of movie reviews which uses a chi-squared feature selection mechanism for training. I show that machine learning algorithms such as Naive Bayes and Maximum Entropy can achieve competitive accuracy when trained using features and the publicly available dataset. It analyse accuracy, precision and recall of machine learning classification mechanisms with chi-squared feature selection technique and plot the relationship between number of…show more content…
Feature Selection

The next step in the sentiment analysis is to extract and select text features. Here feature selection technique treat the documents as group of words (Bag of Words (BOWs)) which ignores the position of the word in the document.Here feature selection method used is Chi-square (x2).
A chi-square test also referred to as a statistical hypothesis test in which the sampling distribution of the test statistic is a chi-square distribution when the null hypothesis is true. The chi-square test is used to determine whether there is a significant difference between the expected frequencies and the observed frequencies in one or more categories.
Assume n be the total number of documents in the collection, pi(w) be the conditional probability of class i for documents which contain w, Pi be the global fraction of documents containing the class i, and F(w) be the global fraction of documents which contain the word w. Then, the x2-statistic of the word between word w and class i is defined[1]
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