Abstract: Sentiment word is the basic unit in the task of sentiment analysis, so sentiment lexicon plays an important role in sentiment analysis. Currently, the sentiment lexicon building methods only use word formation and semantic information, but ignore the context. Based on this, for some words with unknown semantics, it is difficult for traditional semantic methods to obtain the semantic weight, and for some words that have new usage due to context changes, it is difficult to calculate their true weight using semantic methods. To rectify the problem, a sentiment analysis hierarchy from the word to chapter is proposed. Each layer has a representation method and sentiment value calculation formula corresponding to the upper layer, which subdivides the analysis unit from sentence dimensions into word dimensions. Based on this, this paper proposes an automatic construction method for sentiment lexicon based on the character and the context of sentiment word. This method can calculate the weight of sentiment word by using the public sentiment lexicon, the word formation of sentiment word, and the contextual sentiment tendency of sentiment word, and the obtained result is more accurate. Experiments on the real dataset of social networks show that the sentiment unit constructed in this paper has a 3% improvement in accuracy compared with the previous methods. At the same time, the sentiment unit can be used directly in sentiment analysis tasks and the accuracy of sentiment analysis has a 9% improvement in rule-based sentiment analysis experiments and a 3% improvement in deep learning methods.

Key words: sentiment analysis, sentiment hierarchy, sentiment unit, character, context ,