Paper Info
Title
A Sentiment Classification Approach Of Sentences Clustering In Webcast Barrages
Abstract
Conducting sentiment analysis and opinion mining are challenging tasks in natural language processing. Many of the sentiment analysis and opinion mining applications focus on product reviews, social media reviews, forums and microblogs whose reviews are topic-similar and opinion-rich. In this paper, we try to analyze the sentiments of sentences from online webcast reviews that scroll across the screen, which we call live barrages. Contrary to social media comments or product reviews, the topics in live barrages are more fragmented, and there are plenty of invalid comments that we must remove in the preprocessing phase. To extract evaluative sentiment sentences, we proposed a novel approach that clusters the barrages from the same commenter to solve the problem of scattering the information for each barrage. The method developed in this paper contains two subtasks: in the data preprocessing phase, we cluster the sentences from the same commenter and remove unavailable sentences; and we use a semi-supervised machine learning approach, the naive Bayes algorithm, to analyze the sentiment of the barrage. According to our experimental results, this method shows that it performs well in analyzing the sentiment of online webcast barrages.
Year
DOI
Venue
2020
10.3745/JIPS.04.0174
JOURNAL OF INFORMATION PROCESSING SYSTEMS
Keywords
DocType
Volume
Semi-supervised, Sentences Clustering, Sentiment Analysis, Webcast Barrages
Journal
16
Issue
ISSN
Citations 
3
1976-913X
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Jun Li117520.98
Guimin Huang269.26
Ya Zhou3108.62