Emotion classification and language characteristics analysis of "fans community" based on NLP

Xiaomei Sun; Yongsheng Fan

doi:10.1117/12.2624725

13 December 2021 Emotion classification and language characteristics analysis of "fans community" based on NLP

Xiaomei Sun, Yongsheng Fan

Author Affiliations +

Proceedings Volume 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021); 120871P (2021) https://doi.org/10.1117/12.2624725
Event: International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 2021, Kunming, China

Abstract

As abuse and malicious rumor often occur in the " fans community ", which has an extremely bad impact on the society, we need to study the emotional tendency of the comment language of the " fans community " in the network, so as to identify the "loyal fans" and "black fans" and explore the language characteristics of the two types of fans. In this paper, more than 50,000 comments were extracted from common Chinese websites, and some data were pre-processed and manually annotated to construct a Chinese "fans community" comment dataset. The three supervised algorithms and one unsupervised algorithm for" fans community ". Emotional dictionary method are used to classify the " fans community " comment information. Then it is analyzed such as the content of the two types of fans comments in terms of sentences, word count, words, and so on. The experimental results show that all the methods adopted in this paper can effectively classify the comments of "loyal fans" and "black fans" by emotion dichotomy. In terms of language characteristics, the comments of "loyal fans" are characterized by multiple nouns, long sentences and regular comment time. "Black fans" comments are often verbs, short sentences and random comments.

Citation Download Citation

Xiaomei Sun and Yongsheng Fan "Emotion classification and language characteristics analysis of "fans community" based on NLP", Proc. SPIE 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 120871P (13 December 2021); https://doi.org/10.1117/12.2624725

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