Opinion Word Expansion and Target Extraction through Double Propagation

By Guang Qiu, B. Liu, Jiajun Bu, Chun Chen. International Conference on Computational Logic, 2011

1,139 citations recorded by the source corpus.

Analysis of opinions, known as opinion mining or sentiment analysis, has attracted a great deal of attention recently due to many practical applications and challenging research problems. In this article, we study two important problems, namely, opinion lexicon expansion and opinion target extraction. Opinion targets (targets, for short) are entities and their attributes on which opinions have been expressed. To perform the tasks, we found that there are several syntactic relations that link opinion words and targets. These relations can be identified using a dependency parser and then utilized to expand the initial opinion lexicon and to extract targets. This proposed method is based on bootstrapping. We call it double propagation as it propagates information between opinion words and targets. A key advantage of the proposed method is that it only needs an initial opinion lexicon to start the bootstrapping process. Thus, the method is semi-supervised due to the use of opinion word seeds. In evaluation, we compare the proposed method with several state-of-the-art methods using a standard product review test collection. The results show that our approach outperforms these existing methods significantly.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/4fe594f1f0358a00b19e2bb950e45988decab9b7

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. Erik Cambria
2. Soujanya Poria
3. B. Liu
4. Philip S. Yu
5. Hu Xu
6. Bing Liu
7. Lei Shu
8. Devamanyu Hazarika

Citations in the corpus

• An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature (2014, background): “Qiu defined two categories (direct and indirect dependency) to summarize all possible dependencies between two words in sentences [23].”
• Aspect Extraction through Semi-Supervised Modeling (2012, background): “The first subtask extracts aspect terms from an opinion corpus.”
• Aspect extraction for opinion mining with a deep convolutional neural network (2016, methodology): “Popescu stands for (Popescu and Etzioni, 2005) and Prof-dep for (Qiu et al., 2011); P stands for precision and R for recall.”
• Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets (2016, methodology): “We use an existing unsupervised method, called Double Propagation (DP) (Qiu et al., 2011), for extraction.”
• Grammatical structures for word-level sentiment detection (2012, background): “There is also work (Qiu et al., 2011) that uses predefined heuristics over dependency parses to identify both targets and opinion keywords but does not acquire new syntactic heuristics.”
• Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach (2014, methodology): “Qiu et al. (2009; 2011) adopt dependency relations between sentiment words and aspect words.”
• Automated Rule Selection for Aspect Extraction in Opinion Mining (2015, methodology): “The first collection has been widely used in aspect extraction evaluation by researchers [Hu and Liu, 2004; Popescu and Etzioni, 2005; Qiu et al. , 2011; Liu et al. , 2013a].”
• Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction (2018, methodology): “, 2017) and unsupervised approaches (Hu and Liu, 2004; Zhuang et al., 2006; Mei et al., 2007; Qiu et al., 2011; Yin et al., 2016; He et al., 2017).”
• A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, background): “[145] for 4 datasets and yielded more than 89% recall.”
• Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, background): “Proposed approaches for aspect extraction include rule-based strategies (Qiu et al., 2011; Liu et al., 2015), topic models (Mei et al.”
• Lifelong Learning CRF for Supervised Aspect Extraction (2017, background): “…Etzioni, 2005; Zhu et al., 2009), syntactic rules-based extraction (Zhuang et al., 2006; Wang and Wang, 2008; Wu et al., 2009; Zhang et al., 2010; Qiu et al., 2011; Poria et al., 2014), topic modeling (Mei et al., 2007; Titov and McDonald, 2008; Li et al., 2010; Brody and Elhadad, 2010; Wang et…”
• Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews (2017, methodology): “Œe aspect extraction part of OP was evaluated on 5 benchmark online review datasets [21] and it showed the F-score of 0.”
• Survey on Aspect-Level Sentiment Analysis (2016, background): “In [33] (an extended version was published later in [53]), and its extension [34], aspect detection and sentiment lexicon expansion are seen as interrelated problems for which a dou-”
• A survey on classification techniques for opinion mining and sentiment analysis (2017, methodology): “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.”