Sentiment analysis of Persian multi-class tweets based on a combination of semantic relations and support vector machine

With the development of technology, the use of social networks has become more popular. One of the most popular social networks is the Twitter platform. Sentiment analysis of users' tweets plays an important role in showing users' feelings about the existing conditions of society. In recent years, due to the fact that the text of users' tweets have become more conversational, sentiment analysis has become problematic and reduced its accuracy; It also makes natural language processing difficult. In this research, a method for sentiment analysis of Persian tweets based on the combination of semantic relations and support vector machine classification has been presented. FastText semantic relation is used to extract features. Considering that a large number of features have been extracted; They should be reduced, which is done by using long-short-term memory (LSTM) neural network. In the last part of the proposed method for the classification of sentiments in tweets, the support vector machine model is used. The evaluation criteria used in this research were precision, accuracy, recall and F criterion, and the evaluation results were 83.9, 84.3, 83.9 and 84, respectively. The results of the experiments show the applicability of the proposed method in analyzing the emotions of Persian tweets into six classes of anger, sad, joy, disgust, surprise and fear.