Author Archives: Kim Schouten

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 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 […]

Paper Discussion: Zhuang et al. (2006)

Movie Review Mining and Summarization - Li Zhuang, Feng Jing, and Xiao-Yan Zhu Characteristics Summary Domain Movie reviews Sentiment Classes Positive / Negative Aspect Detection Method Custom lexicon plus dependency patterns Sentiment Analysis Method Custom lexicon plus dependency patterns Parser Stanford Parser, using typed-dependencies output Performance Summary Precision: 0.483 Recall: 0.585 F-score: 0.529 Introduction Instead […]