Aspect-Specific Polarity-Aware Summarization of Online Reviews

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  • 1. Aspect-Specific Polarity-Aware Summarization of Online Reviews Gaoyan Ou (ougaoyan@126.com) School of EECS, Peking University 1

2. Outline Motivation Related work The proposed APSM and ME-APSM model Experiments and results Conclusion 2 3. Outline Motivation Related work The proposed APSM and ME-APSM model Experiments and results Conclusion 3 4. Motivation A large amount of reviews which contain peoples opinions However, there are too many reviews to read! Techniques to discover and summarize aspects and sentiments from online reviews are needed It is still a challenging task useless to do analysis manually because of the huge number of reviews the reviews are composed of unstructured texts 4 5. Aspect & Sentiment Extraction ... Aspect 1 (room) pos large clean safe comfortable ... bathroom towels bed shower ... dirty small uncomfortablenoise ...neg Aspect 2 (meal) breakfast fruit eggs juice ... good fresh ...delicious wonderfulpos cold awful terrible poor ...neg Output 1 ... Review n Michelle K Busan, South Korea ...Hilton Wangfujing made my stay in Beijing perfect! The location of the hotel is great. ... The room was large, luxurious and very comfortable... Review 1 Input Sentiment Classification Review n ... Overall sentiment: Aspect-specific sentiment: Review 1 room : meal : staff : Output 2 Problem Setup Aspect and sentiment extraction Aspect extraction Aspect-specific sentiment extraction Sentiment Classification Classify the overall review as positive or negative Key advantage: Figure out how sentiments are expressed according to different polarities for a particular aspect. 5 6. Outline Motivation Related work The proposed APSM and ME-APSM model Experiments and results Conclusion 6 7. Related work Aspect-based sentiment analysis Identify aspects that have been evaluated (aspect extraction) and predict sentiment for each extracted aspects(sentiment extraction) Frequency-based methods (Hu et al. 2004; Popescu et al. 2005) uses frequent pattern mining and a dependency parser to find frequent noun terms and opinions cast on them. Limitation: Produce many non-aspects matching with the patterns Sequential labeling techniques (Jin et al. 2009; Jakob 2010; Choi and Cardie 2010) Employs POS and lexical features on labeled data sets to train a CRF or HMM model Limitation: Need manually labeled data for training. LDA-based methods (ME-LDA (Zhao et al 2010), ME-SAS (Mukherjee and Liu 2012), ASUM (Jo and Oh 2011)) Unsupervised, can extract aspects and sentiments simultaneously. Limitation: cannot extract polarity-aware sentiments for each aspect. 7 8. Outline Motivation Related work The proposed APSM and ME-APSM model Experiments and results Conclusion 8 9. The Proposed Models Two LDA-based aspect and sentiment models Aspect-specific Polarity-aware Sentiment Model (APSM) Improved version of APSM (ME-APSM), which uses a maximum entropy component to better distinguish aspect word from sentiment word Model inference Integrate sentiment and aspect via asymmetric Dirichlet prior 9 10. APSM model z l w r D Nd,s K Sd K K M Graphical representation of APSM model 10 11. x Employing MaxEnt (ME-APSM model) z l w r D Nd,s K Sd K K M Graphical representation of ME-APSM model 11 12. Model inference APSM ME-APSM 12 13. Incorporating Prior Knowledge we expect that no negative word appears in each aspects positive sentiment model positive word will be more likely to appear in each aspects positive model sentiment seeds will get higher prior weights words in the aspect seed list will get higher prior weights sentiment words should unlikely appear in aspect model Sentiment Prior Aspect Prior 13 Asymmetric Dirichlet prior 14. Outline Motivation Related work The proposed APSM and ME-APSM model Experiments and results Conclusion 14 15. Experimental setup 15 16. Qualitative Results Aspect APSM ME-APSM Aspect Senti(p) Senti(n) Aspect Senti(p) Senti(n) Staff staff helpful friendly english desk front good extremely staff friendly courteous helpful attentive clean great recommend unhelpful poor bad noise cold problem overpriced disappointed staff helpful friendly english desk extremely waiter waitress good great helpful friendly excellent wonderful staff clean rude unfriendly unhelpful noise poor disappointed cheap hard Meal breakfast coffee buffet room fruit eggs fresh included breakfast friendly fresh variety good great delicious nice cold scrambled problem hard bad expensive poor die breakfast coffee fruit buffet eggs cheese cereal juice good great fresh hot wonderful excellent nice fantastic cold scrambled awful limited terrible bad poor disappointed Example Aspects and Sentiments Extracted by APSM and ME-APSM 16 Both APSM and ME-APSM can extract coherent aspects and aspect-specific sentiments well. breakfast, coffee, buffet, fruit and eggs are all words related to the aspect meal. In general, ME-APSM performs better than APSM. APSM incorrectly identifies the aspect word staff as positive sentiment words. ME-APSM can discover more specific negative sentiment words, such as rude and unfriendly. 17. Aspect-Specific Sentiment Extraction Aspect/ Sentiment ME-LDA APSM ME-APSM P@5 P@10 P@20 P@5 P@10 P@20 P@5 P@10 P@20 Staff/Pos 1.00 0.90 0.65 0.80 0.70 0.70 1.00 0.80 0.80 Staff/Neg 0.40 0.60 0.35 0.80 0.50 0.35 0.80 0.40 0.30 Room/Pos 0.80 0.60 0.70 1.00 0.90 0.80 1.00 0.80 0.75 Room/Neg 0.60 0.30 0.25 0.40 0.50 0.30 0.80 0.50 0.40 Meal/Pos 0.80 0.80 0.70 0.80 0.80 0.85 1.00 0.80 0.85 Meal/Neg 0.20 0.30 0.30 0.40 0.30 0.35 0.60 0.40 0.30 Avg./Pos 0.87 0.77 0.68 0.87 0.80 0.78 1.00 0.80 0.80 Avg./Neg 0.40 0.40 0.30 0.53 0.43 0.35 0.73 0.43 0.33 Aspect-specific Sentiment Extraction Performance 17 P@n as the metric to compare ME-LDA, APSM and ME-APSM. APSM and ME-APSM give better results than ME-LDA. ME-APSM further outperforms APSM, which suggests the effectiveness of the MaxEnt component. 18. Sentiment Classification Method Hotel Data Set Product Data Set Lexicon-based Method 62.7% 60.2% ASUM 65.6% 64.5% APSM 69.7% 66.5% ME-APSM 72.9% 69.2% APSM+ 70.3% 66.9% ME-APSM+ 73.9% 70.1% Supervised Classification 74.3% 70.7% Sentiment Classification Accuracy 18 Lexicon-based Method counting the positive and negative words in the review Supervised Classification (Denecke 2009): logistic regression ASUM (Jo and Oh 2011) APSM+: APSM with aspect and sentiment seeds ME-APSM+: ME-APSM with aspect and sentiment seeds Lexicon-based method performs worst can not capture the aspect information of the sentiment words. APSM and ME-APSM give better results than ASUM. separating aspects and sentiments improve sentiment classification accuracy ME-APSM further outperforms APSM, which suggests the effectiveness of the MaxEnt component. APSM+, ME-APSM+ > APSM, ME-APSM Incorporating sentiment and aspect prior can improves performance 19. Effect of aspect numbers Sentiment Classification Accuracy with Different Aspect Numbers 19 Sentiment classification performance increases as K increases This trend is more evident on the product data set. 20. Outline Motivation Related work The proposed APSM and ME-APSM model Experiments and results Conclusion 20 21. Conclusion In this paper, we focus on the problem of simultaneously aspect and sentiment extraction and sentiment classification of online reviews. We proposed APSM and ME-APSM to address the problem. Key advantage: extract aspect-specific and polarity-aware sentiment Incorporate sentiment and aspect prior information In the future, we plan to apply our models to more sentiment analysis tasks, such as aspect-level sentiment classification 21 22. Reference Hu, M., Liu, B.: Mining and summarizing customer reviews. In: KDD. (2004) 168177 Jo, Y., Oh, A.H.: Aspect and sentiment unification model for online review analysis. In: WSDM. (2011) 815824 Popescu, A.M., Nguyen, B., Etzioni, O.: Opine: Extracting product features and opinions from reviews. In: HLT/EMNLP. (2005) Zhao, W.X., Jiang, J., Yan, H., Li, X.: Jointly modeling aspects and opinions with a maxent-lda hybrid. In: EMNLP. (2010) 5665 Mukherjee, A., Liu, B.: Aspect extraction through semi-supervised modeling. In:ACL (1). (2012) 339348 Jin, W., Ho, H.H.: A novel lexicalized hmm-based learning framework for web opinion mining. In: Proceedings of the 26th Annual International Conference on Machine Learning. ICML 09, New York, NY, USA, ACM (2009) 465472 Jakob, N., Gurevych, I.: Extracting opinion targets in a single and cross-domain setting with conditional random fields. In: EMNLP. (2010) 10351045 Choi, Y., Cardie, C.: Hierarchical sequential learning for extracting opinions and their attributes. In: ACL (Short Papers). (2010) 269274 Denecke, K.: Are sentiwordnet scores suited for multi-domain sentiment classification? In: ICDIM. (2009) 3338 22 23. Thank you, any question? 23