Phrase Dependency Parsing for Opinion Mining

By Yuanbin Wu, Qi Zhang, Xuanjing Huang, Lide Wu. Conference on Empirical Methods in Natural Language Processing, 2009

436 citations recorded by the source corpus.

In this paper, we present a novel approach for mining opinions from product reviews, where it converts opinion mining task to identify product features, expressions of opinions and relations between them. By taking advantage of the observation that a lot of product features are phrases, a concept of phrase dependency parsing is introduced, which extends traditional dependency parsing to phrase level. This concept is then implemented for extracting relations between product features and expressions of opinions. Experimental evaluations show that the mining task can benefit from phrase dependency parsing.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/f60fc4624e4504b59330ca24501633a9336c9cc9

How researchers see this paper

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

In Artificial Intelligence

1. Lei Shu
2. Hu Xu
3. B. Liu
4. Bing Liu
5. Philip S. Yu
6. Noah A. Smith
7. Aliaksei Severyn
8. Alessandro Moschitti

Citations in the corpus

• Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis (2016, background): “In follow-up work, syntactic relations are further exploited for aspect/opinion extraction (Popescu and Etzioni, 2005; Wu et al., 2009; Qiu et al., 2011).”
• Phrase Dependency Machine Translation with Quasi-Synchronous Tree-to-Tree Features (2014, methodology): “Moving beyond translation, we could use the pool of extracted rules from all systems (and using all parsers) to build monolingual phrase dependency parsers for use in other applications (Wu et al. 2009).”
• Learning Semantic Textual Similarity with Structural Representations (2013, methodology): “Different from (Wu et al., 2009), the collapsed nodes are stored as a shallow subtree rooted at the unified node.”
• Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets (2016, background): “Although many target extraction methods exist (Hu and Liu, 2004; Zhuang et al., 2006; Ku et al., 2006; Wang and Wang, 2008; Wu et al., 2009; Lin and He, 2009; Zhang et al., 2010; Mei et al., 2007; Li et al., 2010; Brody and Elhadad, 2010; Wang et al., 2010; Mukherjee and Liu, 2012; Fang and Huang,…”
• Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms (2017, methodology): “(2011) used a dependency parser to augment a seed collection of aspect and opinion terms through double-propagation, similar for (Popescu and Etzioni 2005; Wu et al. 2009).”
• Modelling input texts: from Tree Kernels to Deep Learning (2015, methodology): “We explore a phrase-dependency tree similar to the one defined in [180].”
• Automated Rule Selection for Aspect Extraction in Opinion Mining (2015, background): “The former is mainly based on dependency relations [Hu and Liu, 2004; Zhuang et al. , 2006; Wang and Wang, 2008; Wu et al. , 2009; Zhang et al. , 2010; Zhao et al. , 2010b; 2014; Liu et al. , 2013b], while the latter on CRF [Jakob and Gurevych, 2010; Choi and Cardie, 2010; Mitchell et al. , 2013]…”
• Opinion Mining and Information Fusion: A survey (2016, result): “There have also been a growing number of studies reporting the successful combination of both approaches [44] [56] [57].”
• Techniques and applications for sentiment analysis (2013, positive): “” Another approach to aspect identification is to use a phrase dependency parser that utilizes known sentiment expressions to find additional aspects (even infrequent ones).(39) We can also view the problem of aspect identification as an information extraction problem and then use a tagged corpus to”
• Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid (2010, methodology): “…0.563 ME-LDA + 10 0.700 0.563 0.488 ME-LDA + 20 0.875 0.650 0.600 ME-LDA + 30 0.825 0.700 0.569 ME-LDA + 40 0.825 0.688 0.581 ME-LDA + 46 0.825 0.700 0.569 quite different training data sets, one from the cell phone domain and the other from the DVD player domain, both used in (Wu et al., 2009).”
• A Survey of Opinion Mining and Sentiment Analysis (2012, neutral): “[111] used dependency tree kernels.”
• A survey on classification techniques for opinion mining and sentiment analysis (2017, background): “…and nouns (Jeyapriya and Selvi 2015; Hu and Liu 2004; Li et al. 2015), Extraction based on exploiting opinion and aspect relations (Qiu et al. 2011; Wu et al. 2009), Extraction based on the supervised learning (Jin et al. 2009; Yu et al. 2011), Extraction based on topic modeling (Vulić et al.…”
• Aspect Extraction through Semi-Supervised Modeling (2012, background): “One approach is to find frequent noun terms and possibly with the help of dependency relations (Hu and Liu, 2004; Popescu and Etzioni, 2005; Zhuang et al., 2006; Blair-Goldensohn et al., 2008; Ku et al., 2006; Wu et al., 2009; Somasundaran and Wiebe, 2009; Qiu et al., 2011).”