- 期刊
- OpenAccess
廣義知網詞彙意見極性的預測
Predicting the Semantic Orientation of Terms in E-HowNet
並列摘要
The semantic orientation of terms is fundamental for sentiment analysis in sentence and document levels. Although some Chinese sentiment dictionaries are available, how to predict the orientation of terms automatically is still important. In this paper, we predict the semantic orientation of terms of E-HowNet. We extract many useful features from different sources to represent a Chinese term in E-HowNet, and use a supervised machine learning algorithm to predict its orientation. Our experimental results showed that the proposed approach can achieve 92.33% accuracy.
參考文獻
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被引用紀錄
蔡怡宣（2017）。以社群輿論管制圖實施公關危機監控〔碩士論文，淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2017.00819
王雅詩（2017）。基於詞性組合的意見字典擴增方法之研究〔碩士論文，淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2017.00608
曹又心（2015）。結合搭配詞與主題概念改善中文口碑分類〔碩士論文，中原大學〕。華藝線上圖書館。https://doi.org/10.6840/cycu201500823
周柏豪（2015）。主題感知的中文概念網情緒預測〔碩士論文，國立臺灣大學〕。華藝線上圖書館。https://doi.org/10.6342/NTU.2015.01546
Tsai, C. Y. (2010). Identify the Sentiment Strength of Words in MicroBlog [master's thesis, National Tsing Hua University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0016-1901201111413176
延伸閱讀
- 陳傳生（2015）。使用廣義知網於情感詞彙之極性分析研究〔碩士論文，國立臺灣師範大學〕。華藝線上圖書館。https://doi.org/10.6345/NTNU202203534
- Huang, H. H. (2014). 中文語篇標記解釋與語篇關係辨識及其在意見極性分析之研究 [doctoral dissertation, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU.2014.00506
- 龐雲黠、苗偉山（2017）。意見領袖的結構極化研究：以新浪微博為例。傳播與社會學刊，(42)，59-90。https://doi.org/10.30180/CS.201710_(42).0004
- Chen, W. (2004). A pervasive information framework based on semantic routing and cooperative caching [master's thesis, The University of Hong Kong]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0029-1812201200004316
- Chueh, C. H., Wang, H. M., & Chien, J. T. (2006). A Maximum Entropy Approach for Semantic Language Modeling. 中文計算語言學期刊, 11(1), 37-55. https://doi.org/10.30019/IJCLCLP.200603.0004