Category Archives: Paper Discussions

This category holds all posts with paper discussions.


Paper Discussion: Titov & McDonald (2008b)

A Joint Model of Text and Aspect Ratings for Sentiment Summarization - Ivan Titov and Ryan McDonald Characteristics Summary Domain Hotel reviews Sentiment Classes 5-star rating Aspect Detection Method Multi-Aspect Sentiment Model (incorporates MG-LDA model) Performance Summary Aspect Detection Aspect service Average Precision: 75.8% Aspect location Average Precision: 85.5% Aspect rooms (with best # of […]


Paper Discussion: Titov & McDonald (2008a) 1

Modeling Online Reviews with Multi-grain Topic Models - Ivan Titov and Ryan McDonald Characteristics Summary Domain Reviews of products, hotels, and restaurants Sentiment Classes 5-star rating Aspect Detection Method MultiGrain LDA Sentiment Analysis Method PRanking (existing method) Performance Summary Sentiment Analysis Ranking Loss: 0.669 Introduction With the advent of topic models, especially, Latent Dirichlet Allocation […]


Paper Discussion: Choi & Cardie (2008)

Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis - Yejin Choi and Claire Cardie Characteristics Summary Domain MPQA corpus Sentiment Classes Positive / Negative Aspect Detection Method N/A Sentiment Analysis Method Support Vector Machine (MIRA) with compositional inference Sentiment Lexicon Wilson et al. (2005) + General Inquirer Performance Summary Polarity Prediction Accuracy: […]


Paper Discussion: Scaffidi et al. (2007)

Red Opal: Product-Feature Scoring from Reviews - Christopher Scaffidi, Kevin Bierhoff, Eric Chang, Mikhael Felker, Herman Ng, and Chun Jin Characteristics Summary Domain Product Reviews Aspect Detection Method Frequency-based, uses baseline statistics Sentiment Analysis Method N/A Performance Summary Precision: 85%-90% Complexity Analysis: O(n) Introduction This work can be seen as following-up on Hu & Liu […]


Paper Discussion: Mei et al. (2007) 1

Topic Sentiment Mixture: Modeling Facets and Opinions in Weblogs - Qiaozhu Mei, Xu Ling, Matthew Wondra, Hang Su, and ChengXiang Zhai Characteristics Summary Domain Weblogs Sentiment Classes Positive / negative Aspect Detection and Sentiment Analysis Topic-Sentiment Mixture Model Performance Summary Positive sentiment model on unseen data: KL-divergence: ~21 Negative sentiment model on unseen data: KL-divergence: […]


Paper Discussion: Ku et al. (2006)

Opinion  Extraction, Summarization and Tracking in News and Blog Corpora - Lun-Wei Ku, Yu-Ting Liang, and Hsin-Hsi Chen Characteristics Summary Domain News items and blogs Sentiment Classes Positive / negative Sentiment Analysis Dictionary-based, using topical relevance filter Performance Summary On NTCIR corpus (news): Precision: 38.06% Recall: 64.84% F1-measure: 47.97% On Blog posts: Precision: 23.48% Recall: […]


Paper Discussion: Kobayashi et al. (2006)

Opinion Mining on the Web by Extracting Subject-Aspect-Evaluation Relations - Nozomi Kobayashi, Ryu Iida, Kentaro Inui, and Yuji Matsumoto Characteristics Summary Domain Car reviews Sentiment Classes N/A Extraction Method Dictionary + Support Vector Machines Performance Summary Opinion extraction: Precision: 67.7% Recall: 50.7% F1-measure: 40.5 Aspect-evaluation pair extraction: Precision: 76.6% Recall: 75.1% Opinionhood determination: Precision: 82.2% […]


Paper Discussion: Du & Tan (2009)

An Iterative Reinforcement Approach for Fine-Grained Opinion Mining - Weifu Du & Songbo Tan Characteristics Summary Domain Chinese Hotel reviews from www.ctrip.com Sentiment Classes unknown Method Improved Information Bottleneck Algorithm for Semantic Information Performance Summary Accuracy of review aspect category construction: Rand-index: 0.71 Sentiment association: Precision: 78.90% Introduction Approaching the problem of aspect-level sentiment analysis […]


Paper Discussion: Baccianella et al. (2009)

Multi-facet Rating of Product Reviews - Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani Characteristics Summary Domain Hotel reviews Sentiment Classes 5-star rating Aspect Detection Method N/A Sentiment Analysis Method Ordinal regression (ε-support vector regression, LibSvm implementation used) Sentiment Lexicon General Inquirer Performance Summary Rating prediction Averaged over all aspects MAEμ: 0.733 MAEM: 1.032 Introduction First […]


Paper Discussion: Ding et al. (2008)

A Holistic Lexicon-Based Approach to Opinion Mining - Xiaowen Ding, Bing Liu, and Philip S. Yu Characteristics Summary Domain Product reviews Sentiment Classes Positive / Negative Aspect Detection Method N/A Sentiment Analysis Method Rule-based Sentiment Lexicon Custom WordNet propagation algorithm Performance Summary Opinion sentence extraction and orientation prediction Precision: 0.91 Recall: 0.90 F-score: 0.90 Introduction […]