Abstract. Different from common texts, microblog texts have large amount of emotions and net-words. Its words and sentences expression are more colloquial and network popular, so traditional sentiment dictionary does not suit for the context of modern microblog short texts. This article puts forward an approach to automatically construct the sentiment dictionary based on microblog emoticons. Collected microblog texts are annotated by emoticons and form sentiment text corpus. To conduct consolidation according to existing sentiment dictionary, extract the sentiment words in the microblog texts per rule of part of speech, calculate the information added value of sentiment words in microblog texts as feature weight, and classify the sentiment words in the method of SVM to get the sentiment dictionary. This article improves the construction method of existing sentiment dictionary. The experimental result shows that the accuracy rate of sentiment dictionary after improvement reaches above 90%, and the overall F value reaches 85%, which are obviously better than existing dictionaries.
Hao et al. REF proposed an approach for automatically constructing a sentiment dictionary based on microblog emoticons by collecting microblog texts that are annotated by emoticons and forming a sentiment text corpus.
An Automatic Construction Approach for Sentiment Dictionary Based on Weibo Emoticons
Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
Computer Science