# A Data-Driven Approach for University Public Opinion Analysis and Its Applications
## 1 Introduction  
Public opinion refers to the views of a majority of people, such as social managers, enterprises, organizations and individuals with respect to the generation, development and impact of social events in the social environment. Public opinion comprises a collection of attitudes, opinions, emotions and other manifestations expressed by the majority of people on various social phenomena and current events. Internet public opinion is generally described as an event that is discussed and considered by many people on the Internet with a considerable social influence, whereby people exhibit tendencies that reflect their psychological state. Internet public opinion is characterized by fast spreading speed, wide spreading routes and considerable social influence [1; 2; 3; 4]. With the rapid development of Internet technology, Internet public opinion is an important barometer for public opinion. University online public opinion is a special kind of public opinion that refers to the opinions, views and emotions expressed by college students on social media with respect to academics, life and social issues. On the one hand, college online public opinion reflects students' views and attitudes toward given events and their values; on the other hand, it reflects students' demands and emotional needs. Given that online social media is usually the source of online public opinion, users with smartphones are able to freely publish their opinions. Because young students are immature and intensely curious about a variety of subject, they can be easily affected by one-sided and radical remarks on the Internet. Consequently, they may be consumed by public opinion and even engage in excessive behaviors that lead to serious consequences [5; 6]. In comparison to traditional media, the content of online data is updated rapidly, with diverse data forms. Users cannot only send messages and upload pictures but also publish videos and animations on the Internet. Therefore, online public opinion is characterized by a massive volume of data and high levels of uncertainty and complexity. High-value information reflecting sentiments of people is hidden in these unstructured data. However, it is time-consuming and inefficient to manually judge users' attitudes hidden among public opinion, and it is difficult to cope with the ever-growing volume of public opinion data over time. Therefore, effective analysis of university online public opinion has become a popular issue [7]. Content sentiment analysis, also referred to as opinion mining, is the process of making judgments about the sentiments and opinions expressed in text through specific methods and classifying expressions into sentiment categories [8]. Because the remarks posted by students on social media contain exhibit a considerable number of subjective emotional tendencies, they directly influence the direction of online public opinion. Content sentiment analysis provides a technical means to analyze users' psychological needs, which drive online public opinion. According to the granularity of processed texts, sentiment analysis can be divided into word-level, phrase-level, sentence-level and chapter-level analyses. Technically, content sentiment analysis methods mainly include sentiment lexicon, machine learning and deep learning [9]. The sentiment-lexicon-based approach is a simple imitation of human memory and judgment processing, largely relying on sentimental words and judgment rules to classify content sentiment tendencies. Although this method is simple and easy to implement, it is still limited because words have diverse meaning, and sentimental words are absent. With the increasing development of natural language processing technology and computer science, the machine-learning-based content sentiment analysis method has become an important solution for online public opinion analysis [10].