# A Data-Driven Approach for University Public Opinion Analysis and Its Applications
## 2 Related Works  
Online opinion identification is important work. DARPA proposed the TDT (Topic Detection and Tracking) project in 1996 to identify unknown topics in news and track known topics [12]. [PERSON] and [PERSON] [13] showed that government, networks and media can influence the propagation path of public opinion events, providing theoretical support for public opinion governance. However, public opinion research is usually oriented toward the government, and relatively less attention has been paid to university public opinion. Research on university online public opinion involves four main aspects: public opinion information collection, topic detection, opinion mining and content tendency analysis. The authors of [14; 15; 16] proposed sentimental-lexicon-based classification methods, whereby researchers used statistics indices, including maximum entropy and mutual information, to describe the similarity between the text to be quantitatively detected and the words in the sentiment lexicon and subsequently used manually formulated rules to judge the sentiment tendency. This method is relatively simple and intuitive. However, building a complete sentimental lexicon and rules is too complicated and strongly relies on the designer's a priori knowledge, which is only applicable to some specific fields and difficult to extended to wider ranges. Machine learning methods can transform text into digital signals that computers can recognize; then, related algorithms can be used to realize content sentiment classification. [PERSON] et al. first used machine learning algorithms for sentiment analysis of movie comments [17]. Results found that the accuracy and recall rates of machine learning methods, such as support vector machine and naive Bayes, on the IMDb dataset outperformed knowledge-based algorithms, verifying the feasibility of machine learning for sentiment classification. [PERSON] proposed a text modelling method based on latent Dirichlet allocation (LDA) [18]. With this method, probability distributions can be used to quantitatively describe the distribution of topics. Accordingly, machine learning classification algorithms can accomplish the task of opinion analysis [19; 20]. In ref. [21], public opinion data collected from Baidu Post Bar were treated by the LDA model for text feature extraction, followed by cosine similarity calculation. According to the similarity results, the Krill Herd Harmony search optimization algorithm was used to help a CNN improve classification accuracies. In ref. [5], a machine learning and evolutionary algorithm-based college student public opinion analysis method was proposed, whereby singular value decomposition was applied to perform coordinate transformation within the sample space to determine the abstract meaning of original features. Then, a basic BP-NN was established to perform forward inference after optimizations by the evolutionary algorithm. Inter-information was selected as the text feature classification index for student public opinion management of campus commentary [6]. Although machine-learning-based approaches are usually superior to sentiment-lexicon-based approaches, the disadvantages of machine learning include that training relies on high-quality labelled datasets and appropriate selections of text features (including but not limited to \(n\)-gram features, TF-IDF features, TFC features, Boolean weights, etc. ), which are not easy to handle with a high volume of complex text data. With the rapid development of computer science and big data technology, deep learning has made breakthrough achievements in many fields [22]. Compared with traditional machine learning and sentiment-lexicon-based methods, the advantage of deep learning techniques is that deep networks can automatically extract text features from input data without the need for prior knowledge and manual feature engineering. In ref. [23], a parallel neural network structure was proposed for sentiment classification of MOOC discussion forums.