Creating a Sentiment Analyzer for Text Messages

Abstract

The work is devoted to the use of Logistic Regression and Neural Network methods to develop a methodology for creating and implementing an analyzer for determining the sentiment of a text in Armenian. Based on a selection of data from social networks, this analyzer determines the tone of the message entered by the user.  Note that such work with Armenian texts is carried out for the first time. Sentiment analysis of the text helps to form an opinion about the content of the message without reading the entire text, freeing the user from unnecessary work and time. Sentiment analyzer based on various machine-learning methods for text classification has been created. The paper presents the results of a comparative analysis of the models underlying this analyzer. The created analyzer is used in processing the results of a social survey of students both to determine the students’ opinion about the relevance of teaching a given subject and to assess the quality of teaching a subject by a teacher. It is also supposed to use the analyzer to determine the psychological state of the student before the exam in order to adapt the exam to student.