SENTIMENT ANALYSIS OF THE DRUG REVIEWS
A program was designed to make a sentiment analysis of the drug reviews using supervised learning. Upon the completion of the project, we estimated the balance (positive/negative/neutral) of the review classes in the web among the drug consumers. For the training of the classifier, the optimal ratio of the classes was found in the training sample. During the research, we drafted a set of rules to prepare the text of the reviews for the construction of the vector. The following rules are proposed: the translation of the entire text to lowercase, the exception of stopwords (pronouns, particles, and pronouns), and the processing of negation in the sentence. For the program we also compiled unigram dictionaries: the frequency dictionary and the dictionary created using pointwise mutual information method (PMI). The results of the classifier's work for each combination of the studied parameters of the classification system for consumers' emotional responses are presented in the article.