Cross-Domain Co-Extraction of Sentiment and Topic Lexicons

By Fangtao Li, Sinno Jialin Pan, Ou Jin, Qiang Yang, Xiaoyan Zhu. 2012

212 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Bing Liu
2. Shuai Wang
3. Zixuan Ke
4. Duyu Tang
5. B. Liu
6. Hu Xu
7. Lei Shu
8. Zhiyuan Chen

Citations in the corpus

• Lifelong Learning for Sentiment Classification (2015, background): “Other works along similar lines include (Andreevskaia and Bergler, 2008, Bol-legala et al., 2011, He et al., 2011, Ku et al., 2009, Li et al., 2012, Li et al., 2013, Pan and Yang, 2010, Tan et al., 2007, Wu et al., 2009, Xia and Zong, 2011, Yoshida et al., 2011).”
• Forward and Backward Knowledge Transfer for Sentiment Classification (2019, background): “Our work is also closely related to transfer learning (TL) (Pan and Yang, 2010; Blitzer et al., 2007; Andreevskaia and Bergler, 2008; Bollegala et al., 2011; He et al., 2011; Li et al., 2012, 2013; Xia and Zong, 2011; Pan et al., 2010; Wu et al., 2009, 2017; Augenstein et al., 2018).”
• Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach (2014, methodology): “Sentiment lexicon is a fundamental component for sentiment analysis, which can be built manually (Das and Chen, 2007), through heuristics (Kim and Hovy, 2004) or using machine learning algorithms (Tur-ney, 2002; Li et al., 2012; Xu et al., 2013).”
• Hybrid Heterogeneous Transfer Learning through Deep Learning (2014, background): “The reason is that, opinion and topic words used on different types of products can be very different (Li et al. 2012).”
• Learning Sentence Embeddings with Auxiliary Tasks for Cross-Domain Sentiment Classification (2016, background): “Another line of work tries to derive domain-specific sentiment words (Bollegala et al., 2011; Li et al., 2012).”