Lexicon-based approach to the Sentiment and Emotion Analysis of a text in Azerbaijani language

Abstract

Sentiment Analysis of a text and Emotion Analysis of a text in the Azerbaijani language has been recently at the center of attention as a research topic. Although different approaches have been applied to this topic, there are still many approaches exist that can be applied to it. In this research, the lexicon-based approach is applied to both Sentiment and Emotion Analysis. Briefly, sentiment analysis deal with whether a text is positive, negative, or neutral, while emotion analysis detects one of the basic emotions. Note that, in this research basic emotions of Plutchik's Wheel of Emotions are used in order to categorize emotions which are anger, anticipation, disgust, fear, surprise, trust, sadness, and joy. This research paper used 24,000 lines of news in Azerbaijani for training and testing the models and used 66,000 lines of newly created Azerbaijani word dictionary dataset in order to apply a lexicon-based approach. From the implementation point of view, Decision Trees, Naïve Bayes, Support Vector Machine, Neural Network, and BERT machine learning algorithm are applied and compared. Moreover, this research also deals with entity recognition. It detects the corresponding sentiment and list of emotions of the main entity of the text. Note that, in this research, entity recognition is accomplished using a rule-based approach.