Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (6): 32-45    DOI: 10.11925/infotech.2096-3467.2021.0793
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Sentiment Curve Clustering and Communication Effects of Barrage Videos
Zhang Teng1,2,Ni Yuan1,2(),Mo Tong3,Lv Xueqiang4
1School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192, China
2Beijing Knowledge Management Research Base, Beijing 100192, China
3School of Software and Microelectronics, Peking University, Beijing 102600, China
4Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100192, China
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Abstract

[Objective] This paper constructs a clustering model for sentimental time series of bullet screen texts, aiming to predict video communication effects. [Methods] First, we used the Word2Vec to expand the sentiment dictionary and optimize the performance of sentiment classifiers. Then we added comprehensive weights to make the sentiment sequence smooth and stable. Finally, we constructed the SBD measurement and K-shape clustering model to analyze sentiment sequence patterns, characteristics, and communication effects. [Results] The optimized model had F1 values of 0.89 and 0.79 with multi-classification indicators (subjective or objective, and polar classification). The performance of the subjective and objective classifier was improved by 123%. Compared with the existing multiple time series measurement clustering algorithms, the proposed new model generated better Davies-Bouldin Index and Silhouette Index. [Limitations] The new algorithm did not fully utilize the Internet buzzwords or sentence situations without central adjectives. The description and interpretation of sentimental time series clustering results need to be further explored. [Conclusions] The proposed model could reduce the irregular noise and the timing phase shift of the bullet screen texts, while the clustering results are the basis for identifying the different effects.

Received: 04 August 2021      Published: 28 July 2022
 ZTFLH: TP393 G250
Fund:Beijing Social Science Foundation(21GLB027)
Corresponding Authors: Ni Yuan     E-mail: niyuan230@163.com