Emotional Behavior Analysis of Music Course Evaluation Based on Online Comment Mining

Introduction

An emotional dictionary is a collection of different emotional words marked with emotional tendencies. Generally speaking, an emotional dictionary marks the positive and negative degree of emotional words. The emotional words in the emotional dictionary can be used to judge the emotional tendency of the text, so constructing the emotional dictionary is an indispensable part of the analysis of the emotional tendency of the text. Through machine learning or natural language processing technology, an emotional dictionary can separate people's opinions, feelings, evaluations, attitudes and emotions about entities or attributes from texts (Chen et al., 2020). At present, the sentiment dictionary is widely used in consumer product analysis, social public opinion monitoring, stock market forecasting and customer feedback tracking, and its main analysis methods are semantic-based and machine learning-based methods. Semantic-based method mainly calculates the emotional value of the text through the emotional dictionary and then determines the emotional tendency of the text. Music teaching evaluation plays a guiding and quality monitoring role in music curriculum reform, which is the crucial factor in the success or failure of the music curriculum reform and an indispensable link in the whole music teaching work (Zhou et al., 2021). The goal of the music curriculum is achieved through teaching and various music practice activities. The “practical activity” here is a process, a longitudinal comparison of self-development for individual students. Due to the influence of social and family education and other environmental factors, many students lack the perception of music. The evaluation of music courses not only has the functions of guiding, identifying, inspiring, improving, and regulating music teaching, but also plays an important role in the development of students' music quality, the improvement of music teachers' teaching level, and the improvement of school music education and teaching management. Evaluation of students is the transformation of subject and object, which makes students become the main body in the process of appreciation. In the past appreciation classes, teachers' evaluations often replaced students' evaluation, mainly because teachers ignored students' potential to appreciate music and dance.

With the popularity of the Internet in life, people's lifestyles have changed a lot. Listening to music in leisure time is one of most people's choices. Music is an expression carrier of emotion, and many songwriters express their emotions through music. It can be said that emotion is music's essential feature and connotation. Nowadays, the Internet has many texts containing much emotional information, such as commodity evaluation information and comments on important events (Gui et al., 2019).

This paper analyzes the evaluation of music courses and emotional behaviors based on an emotional dictionary under online review mining. Online review is an important part of users' original content, which refers to the comments of ordinary users on something on the Internet. With the continuous enrichment of network forms, online comments have become various, which can be divided into two forms: well-structured online comments and unstructured online comments. The purpose of online comment mining is to mine helpful information based on the massive comment information in the network, which can be described from three aspects: comment feature mining, comment text sentiment analysis, and comment text topic recognition (Bedoya et al., 2021). Attitudes and values, as the primary goal of the music curriculum, highlight the essence of music education as aesthetics. The music course’s education mode is to educate people by emotion and aesthetic education. Its educational effect lies not in acquiring knowledge and skills, but in enriching students' emotional experience and cultivating students' interest in music and emotional reflection. As an essential part of text mining, text topic extraction is a process of selecting some keywords in the text to represent the content of the text topic to extract the topic of the document (Gupta & Lehal, 2009). Its essence is a probability model, and the distribution of the document topic can be obtained by using an efficient probability inference algorithm, which is suitable for processing large-scale text sets and corpora (Cores-Bilbao et al., 2019).