# Research on Sentiment Analysis of Online Public Opinion Based on RoBERTa-BiLSTM-Attention Model
## 2 Literature Review  
Sentiment classification is the process of identifying the emotional tendency of netizen comments with subjective emotions and analysing them. There are many is an abundance of research on the sentiment classification of opinion netizen comments, and there are currently four mainstream methods of sentiment classification. (1) Sentiment classification based on a sentiment dictionary. This involves dividing the netizen comments into words and then scoring them according to the rules after matching them with the sentiment values in the dictionary, and the average value of the divided words' sentiment is taken as the sentiment score of the comments [1]. The development of English sentiment dictionaries is early and mature, including mainly Senti Word Net, Opinion Lexicon, MPQA, etc. [2]; the more widely used Chinese sentiment dictionaries are Dalian Polytechnic Sentiment Dictionary, Zhi.com How Net, NTUSD of National Taiwan University, etc. [3]. However, the coverage of the basic dictionary is limited, and some scholars have begun to manually construct emotion dictionaries to enrich the corpus. Scholars such as [PERSON] constructed a high-coverage, semi-supervised dictionary containing reinforcing words to enhance sentiment classification accuracy [4]. Scholars such as [PERSON] used Word2 vec to classify emotion words by considering emotional, semantic, and positional features to obtain a dynamic dictionary [5]. There are also scholars who constructed a text-image-audio multimodal lexicon to improve the accuracy of sentiment analysis, e.g., [PERSON] captured the text, image, and audio features of video data and performed multimodal fusion to identify the sentiment of opinionated netizens [6]. In addition, there are scholars who constructed domain dictionaries to improve domain adaptation. Two scholars, [PERSON] and [PERSON], combined the weighted point-by-point mutual information method with negative words and reversed sentiments to automatically construct a financial sentiment dictionary [7]. Such methods can achieve good classification results and are easy to understand in the absence of a large number of datasets, but their accuracy depends on the coverage of the sentiment lexicon, with problems such as poor domain adaptability and an inability to solve the problems of Chinese sentiment analysis due to, for example, words with multiple meanings and ironic phrases. This kind of method mostly builds domain dictionaries, emoji dictionaries, etc., to expand the scope or is used as an auxiliary method in combination with other methods. (2) Sentiment classification based on machine learning. Sentiment classification based on machine learning is the process of extracting effective features of Internet users' comments for sentiment classification using machine learning algorithms. Commonly used shallow machine learning methods are support vector machine, plain Bayes, and KNN methods [8]. [PERSON] and other scholars used support vector machines to automatically identify the sentiment of citations based on features such as citation location and sentiment words [9]. [PERSON] and other scholars developed a hybrid framework integrating sentiment analysis and machine learning techniques designed to detect conversation polarity switches and predict customer sentiment at the end of a conversation with a service provider, helping companies improve customer satisfaction and enhance customer engagement [10]. [PERSON] deployed different combinations of five machine learning algorithms (Random Forest, Logistic Regression, Decision Tree, LinearSVC, and Naive Bayes) and three vectorisation methods (Doc2 Vec, CountVectorizer, and TF-IDF) for sentiment classification, and their experimental results showed that TextBlob + TF-IDF + LinearSVC had the best performance [11].