Structure-Aware Review Mining and Summarization

By Fangtao Li, Chao Han, Minlie Huang, Xiaoyan Zhu, Yingju Xia, Shu Zhang, Hao Yu. International Conference on Computational Linguistics, 2010

361 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1256c05bd50a80bb0a223ca94674c71fd61fad5a

How researchers see this paper

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

1. B. Liu
2. Lei Zhang
3. Charu C. Aggarwal
4. Bing Liu
5. Arjun Mukherjee
6. Qiang Yang
7. Hu Xu
8. Sinno Jialin Pan

Citations in the corpus

• Aspect Extraction through Semi-Supervised Modeling (2012, methodology): “Another approach is to use supervised sequence labeling (Liu, Hu and Cheng 2005; Jin and Ho, 2009; Jakob and Gurevych, 2010; Li et al., 2010; Choi and Cardie, 2010; Kobayashi et al., 2007; Yu et al., 2011).”
• Social Collaborative Viewpoint Regression with Explainable Recommendations (2017, unknown): “Following [21], we assume that the sentiment label lj for a word wj depends on the topic zj .”
• FLAME: A Probabilistic Model Combining Aspect Based Opinion Mining and Collaborative Filtering (2015, background): “Much work has been proposed to help users digest and exploit large number of reviews by aspect-based opinion mining techniques, including information extraction from reviews [11], uncovering the latent aspects of the review sentences [13], inferring the latent aspect ratings and aspect weights of ea”
• Neural Network-Based Abstract Generation for Opinions and Arguments (2016, background): “summarization work (Zhuang et al., 2006; Li et al., 2010).”
• An Unsupervised Neural Attention Model for Aspect Extraction (2017, background): “Jin and Ho (2009) and Li et al. (2010) proposed to use hidden Markov models (HMM) and conditional random fields (CRF), respectively with a set of manually-extracted features.”
• Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms (2017, methodology): “Li et al. (2010) and Jin and Ho (2009) implemented CRF and HMM with extensive human-designed features to solve the problem, respectively.”
• Sentiment Analysis and Opinion Mining (2012, methodology): “Li et al. (2010) took a different approach.”
• Survey on Aspect-Level Sentiment Analysis (2016, background): “The same idea can be found in [59], a CRF model discussed in Section 3.”
• Cross-Domain Co-Extraction of Sentiment and Topic Lexicons (2012, methodology): “Jin and Ho (2009) and Li et al. (2010a) both proposed to use supervised sequential labeling methods for topic and opinion extraction.”
• A Rule-Based Approach to Aspect Extraction from Product Reviews (2014, unknown): “Li et al. (Li et al., 2010), Choi and Cardie (Choi and Cardie, 2010) and Huang et al. (Huang et al., 2012) also used CRF for extraction of explicit aspects.”
• Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier (2016, background): “[9] formulated mining the review task as a joint structure tagging problem, focussing on object feature based summarization.”
• Gated Neural Networks for Targeted Sentiment Analysis (2016, methodology): “Targeted sentiment analysis is related to fine-grained sentiment analysis (Wiebe, Wilson, and Cardie 2005; Jin, Ho, and Srihari 2009; Li et al. 2010; Yang and Cardie 2013; Nakov et al. 2013), which extracts opinion expressions, holders and targets jointly from given sentences.”