An empirical study of sentiment analysis for chinese documents
By Songbo Tan, Jin Zhang. Expert systems with applications, 2008
506 citations recorded by the source corpus.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/346d900f2f73b706ec539caecb4800d131fc9f6a
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In Artificial Intelligence
Citations in the corpus
- A Review of Sentiment Analysis Research in Chinese Language (2017, unknown): “ICTCLAS was developed by Dr. Zhang Huaping and is considered as the benchmark in Chinese word segmentation [27].”
- Using text mining and sentiment analysis for online forums hotspot detection and forecast (2010, methodology): “It is recognized that the performance of sentiment classifiers are dependent on domains or topics [22].”
- A feature selection model based on genetic rank aggregation for text sentiment classification (2017, background): “Tan and Zhang [5] empirically evaluated the effectiveness of four feature selection methods (document frequency, chi-square statistics, mutual information and information gain) on five learning algorithms (centroid classifier, K-nearest neighbour algorithm, winnow classifier, Naı¨ve Bayes…”
- Revisiting Pre-Trained Models for Chinese Natural Language Processing (2020, unknown): “We use the ChnSentiCorp dataset for evaluating sentiment classification, where the text should be classified into either a positive or negative label, for evaluating sentiment classification performance.”
- Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews (2012, background): “…& Li, 2010; Hwang & Ko, 2008; Kang, Yoo, & Han, 2009; Kim, Jung, & Mayeng, 2008; Miao, Li, & Dai, 2009; Pang et al., 2002; Prabowo & Thelwall, 2009; Tan & Zhang, 2008; Turney, 2002; Ye et al., 2008; Zhu, Xu, & Wang, 2010), the positive and negative classification research of the feature-level…”