Building a Sentiment Summarizer for Local Service Reviews

By Sasha Blair-Goldensohn, Kerry Hannan, Ryan T. McDonald, Tyler Neylon, George A. Reis, Jeffrey C. Reynar. 2008

449 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f447fccf4b3289bdbcbdee63bd43082c4c12d157

How researchers see this paper

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

1. Lei Zhang
2. B. Liu
3. Arjun Mukherjee
4. Xin Li
5. Lidong Bing
6. Kim Schouten
7. Wai Lam
8. Saif M. Mohammad

Citations in the corpus

• Aspect-based sentiment analysis of movie reviews on discussion boards (2010, background): “In most cases, a sentence level sentiment analysis is more sophisticated than a document level one, and representative works include [26–35].”
• Twitter Polarity Classification with Label Propagation over Lexical Links and the Follower Graph (2011, unknown): “Several have used label propagation starting with a small number of hand-labeled words to induce a lexicon for use in polarity classification (Blair-Goldensohn et al., 2008; Rao and Ravichandran, 2009; Brody and Elhadad, 2010).”
• Transformation Networks for Target-Oriented Sentiment Classification (2018, methodology): “The early methods mostly adopted supervised learning approach with extensive hand-coded features (Blair-Goldensohn et al., 2008; Titov and McDonald, 2008; Yu et al., 2011; Jiang et al., 2011; Kiritchenko et al., 2014; Wagner et al., 2014; Vo and Zhang, 2015), and they fail to model the semantic rela”
• Survey on Aspect-Level Sentiment Analysis (2016, background): “Contrary to the above, a form of parallel hybridization can be found in [52], where a MaxEnt classifier is used to find the frequent aspects, for which there is ample data, and a rule-based method that uses frequency information and syntactic patterns to find the less frequent ones.”
• Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora (2016, background): “, review scores, emoticons) specific to certain domains (Asghar et al., 2015; Blair-Goldensohn et al., 2008; Bravo-Marquez et al., 2015; Choi and Cardie, 2009; Severyn and Moschitti, 2015; Speriosu et al., 2011; Tang et al., 2014).”
• A Rule-Based Approach to Aspect Extraction from Product Reviews (2014, methodology): “Hu and Liu’s method was improved by Popescu and Etzioni (Popescu and Etzioni, 2005) and by Blair-Goldensonh (Blair-Goldensohn et al., 2008).”
• Extracting and Ranking Product Features in Opinion Documents (2010, unknown): “Feature frequency has been considered in (Hu and Liu, 2004; Blair-Goldensohn et al., 2008).”
• Sentiment Analysis and Opinion Mining (2012, methodology): “, 2006), and to summarize service reviews (Blair-Goldensohn et al., 2008).”
• Learning Attitudes and Attributes from Multi-aspect Reviews (2012, unknown): “Lexicon discovery is discussed in [25], and has been used for review summarization by [3], though such approaches require considerable manual intervention, and are not learned automatically.”
• 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).”
• More than words: Social networks' text mining for consumer brand sentiments (2013, methodology): “Blair-Goldensohn et al. (2008) used Google Maps data as input in order to analyze consumer sentiments towards hotels, department stores and restaurants.”