Building Chinese Sentiment Lexicon Based on HowNet

2011 ◽  
Vol 187 ◽  
pp. 405-410 ◽  
Author(s):  
Yu Wei Liu ◽  
Shi Bin Xiao ◽  
Tao Wang ◽  
Shui Cai Shi

Judging the sentiment orientation of Chinese words is the basic work of the passage sentiment orientation research. Using Chinese basic sentiment words and corpus, we can identify sentiment words in the passage and expand sentiment lexicon effectively in order to improve the result of text semantic orientation analysis. With the basis of HowNet [1] sentiment words, we construct a Chinese sentiment lexicon by analyzing sentence structure and calculating the score of semantic similarity. We conduct Chinese text sentiment orientation classification experiment with this lexicon, the result shows the accuracy has achieved above 70% and obtained quite good classification effect.

2014 ◽  
Vol 13 (9) ◽  
pp. 1612-1621 ◽  
Author(s):  
Yun Pan ◽  
Xiaojun Li ◽  
Hanxiao Shi ◽  
Hong Liu

10.29007/f4j4 ◽  
2018 ◽  
Author(s):  
Behnam Sabeti ◽  
Pedram Hosseini ◽  
Gholamreza Ghassem-Sani ◽  
Sَeyed Abolghasem Mirroshandel

Sentiment analysis refers to the use of natural language processing to identify and extract subjective information from textual resources. One approach for sentiment extraction is using a sentiment lexicon. A sentiment lexicon is a set of words associated with the sentiment orientation that they express. In this paper, we describe the process of generating a general purpose sentiment lexicon for Persian. A new graph-based method is introduced for seed selection and expansion based on an ontology. Sentiment lexicon generation is then mapped to a document classification problem. We used the K-nearest neighbors and nearest centroid methods for classification. These classifiers have been evaluated based on a set of hand labeled synsets. The final sentiment lexicon has been generated by the best classifier. The results show an acceptable performance in terms of accuracy and F-measure in the generated sentiment lexicon.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 134964-134975
Author(s):  
Yan Cheng ◽  
Leibo Yao ◽  
Guoxiong Xiang ◽  
Guanghe Zhang ◽  
Tianwei Tang ◽  
...  

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