# Weibo Text Sentiment Analysis Based on BERT and Deep Learning
## 1 Introduction  
The emergence of a large number of online public opinions is the product of people expressing their own opinions in response to various events, and the resulting online public opinions are related to the development of things. Sina Weibo (referred to in this paper as 'Weibo'), as one of the social platforms with the most users in China, carries a large amount of online public opinion. Users post their views on things in the form of short texts on the Weibo platform for other users to browse, comment on and forward. In addition, users can also search for related events by keywords. Through these methods, certain events that attract people's attention can be spread quickly and widely, and then generate public opinion on the internet. Therefore, Weibo is regarded as a platform for the public to collect and publish information and learn about social knowledge to manage uncertainty and risk [1], hence sentiment analysis of Weibo text is critical.  
Text sentiment analysis refers to analyzing the emotional information contained in a text and classifying it into its category, and the classification results can be applied to some other downstream tasks. It plays a vital role in monitoring online public opinion. Analyzing the emotional tendency of short texts posted by Weibo users can not only aid understanding of users' views on things and grasp user psychology, but also further control the trend of online public opinion and improve government credibility. It also can speed up the government's response to online public opinion.  
Recently, sentiment analysis has been a research hotspot in the field of data mining and natural language processing [2]. Therefore, many methods for sentiment analysis were born. At present, commonly used text sentiment analysis methods are divided into early rule-based methods, machine learning methods, and the currently more popular deep learning methods. Rule-based methods include methods based on sentiment dictionary, and soforth. This method requires artificial construction of sentiment dictionary [3]. However, due to the endless emergence of a large number of online new words and new ideas in the internet, the constructed sentiment dictionary can not meet the needs, so this method cannot achieve a high accuracy rate [4]. Methods based on machine learning include support vector machine, decision tree, and so forth, which also require a certain labor cost, and the accuracy of classification depends on the results of feature extraction. However, due to the continuous emergence of massive information, it is difficult to fully extract text features, making the classification accuracy drop. Compared with the traditional machine learning methods, the deep learning technology that appeared later has achieved more successful results in the fields of computer vision [5], machine translation [6], and text classification [7]. Methods based on deep learning algorithms such as CNN and LSTM do not require manual intervention, and the classification accuracy is high but it needs to use a large scale dataset to train the model, and it is difficult to construct a large scale and high-quality training dataset. At present, many researchers combine rule-based methods with machine learning or deep learning techniques. They have achieved satisfactory results in terms of accuracy, but there are often problems such as excessively complex models [8].  
In view of the shortcomings of the above methods, in order to increase the accuracy of the text sentiment analysis model, based on the BERT and related deep learning techniques, this paper proposes a new sentiment analysis model. Experimental results show that this model has significantly improved accuracy compared with other similar models.