Extracting key sentiment sentences from internet news via multiple source features

Extracting key sentences with sentiments from discourses plays an important role in sentiment analysis. Different from general discourses, Internet news has its own fashion of sentiment expression. In this paper, we attempt to extract key sentiment sentences from those Internet news articles. In thi...

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Bibliographic Details
Published in2014 4th IEEE International Conference on Network Infrastructure and Digital Content pp. 126 - 130
Main Authors Feng Liangzu, Li Ruifan, Zhou Yanquan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2014
Subjects
Accuracy
Context
Data mining
Dictionaries
Feature extraction
Internet
key sentiment sentences
multiple source features
score
Semantics
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Summary:Extracting key sentences with sentiments from discourses plays an important role in sentiment analysis. Different from general discourses, Internet news has its own fashion of sentiment expression. In this paper, we attempt to extract key sentiment sentences from those Internet news articles. In this paper, we propose a method, called MSF, by using multiple sources features. In our method, for each sentence we first design four sources of features, including lexical sentiment, global position, word grammar indicator, and title similarity. Then, these features are linearly combined to obtain a score indicating the probability that the sentence is a key sentiment sentence. Experiments on a publicly available dataset show the effectiveness of our MSF method.
ISSN:2374-0272
DOI:10.1109/ICNIDC.2014.7000279