# Enhancing Short-Term Sales Prediction with Microblogs: A Case Study of the Movie Box Office
## 3 Framework of the Microblog-Based Short-Term Sales Prediction
### Sentiment Analysis  
Sentiment can be expressed through text, and text is usually a sentence or a combination of multiple sentences. Sentences are composed of words and phrases. Some words are emotional, e.g., \"happy\" means happy, \"roar\" means angry. Additionally, sentences  
Figure 1: The architecture of microblog-based short-term sales prediction. composed of words may be emotional. For example, \"the woman is so beautiful\" means the user praises the woman. The sentiment of the text reflects the user's viewpoint and attitude towards things, the sentiment of positive tendency reflects the user's positive attitude, and the sentiment of negative tendency reflects the user's negative attitude. We can analyze the sentiment of the text to obtain the emotion held by the user. Sentiment analysis refers to analyzing the emotional state implied by the user when transmitting information and judging or evaluating the user's attitude and opinions [27]. Text can be divided into subjective text and objective text. Objective text is a text without any emotional factor, and subjective text can be divided into positive tendency text and negative tendency text. Whether it is a single word or the whole text, the sentiment polarity of text can be obtained through sentiment analysis. According to the different text granularity, sentiment analysis mainly has four research directions: word-level sentiment analysis, sentence-level sentiment analysis, text-level sentiment analysis, and topic-level sentiment analysis. The word-level sentiment analysis is the basis of the text sentiment analysis. Compared with the sentiment analysis of conventional textual documents, microblog text is short, and the microblog content often includes irregular words. In addition, microblog users often invent new symbols or words, complicating microblog text analysis. For example, there is a common phenomenon of polysemy of one word, and the meaning of the same word in different contexts is likely to be the opposite. Notably, the Chinese microblog's short and casual nature brings great challenges to text sentiment analysis. A sentiment dictionary is useful to improve the efficiency of sentiment analysis because we can directly judge the polarity of the corresponding words based on the sentiment dictionary. So far, the sentiment polarity of each microblog can be calculated by defining a list of negative, positive, or neutral words. Generally, there are two kinds of text sentiment classification methods: sentiment calculation based on a sentiment dictionary and sentiment classification based on statistical learning. The latter method needs to label the training data manually, and there are certain human subjective factors. Therefore, this paper uses the former method to classify the text objectively. Sentiment calculation based on the sentiment dictionary is to judge the sentiment of words through the sentiment dictionary and then judge the sentiment polarity of text. The basic idea of this method is to regard the emotional polarity of words with a positive tendency as 1, the sentiment polarity of words with a negative tendency as \(-1\), and the sentiment polarity of other words as 0. Each word contributes the same to the analysis of text tendency. By accumulating the sentiment polarity of each word in the text, we can determine the sentiment polarity of the text. Although a microblog only contains simple sentences, it will have a clear topic and may involve multiple topics expressing different views or opinions. However, the sentiment polarities on these sub-topics may be different. Thus, the sentiment polarity of the sub-topic may affect the emotional calculation of the whole text. Therefore, in this paper, we define the weight of the words in microblog sentiment analysis through the topic similarity between words and the microblog to obtain the sentiment polarity of the microblog. In particular, we use the LDA (Latent Dirichlet Allocation) model for the topic analysis of microblogs [1].