Aspect-Specific Polarity-Aware Summarization of Online Reviews
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Aspect-Specific Polarity-Aware Summarization of Online Reviews

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  • Good afternoon, everyone! I’m Gaoyan Ou from Peking University. And now I am a PhD student. My talk today is “Aspect-Specific Polarity-Aware Summarization of Online Reviews”.
  • Here is my outline. First I will talk about the motivation of our work. And then give a review of the related research. Next I propose two probabilistic models named APSM and ME-APSM to model the online reviews. After that, I will describe the experiments and results. Last, I’ll give the conclusion.
  • First, let’s look at our motivation. With the rapid development of various types of social media, there exists a large amount of reviews which contain people’s opinions. The opinions are important for both customers and manufacturers. However, the number of reviews is so huge for user to digest. We need techniques to automatically discover and summarize aspects and sentiments for us. It’s still a challenging task.
  • Now, I will describe the problem we want to address. Given a collection of reviews about a product. We want a structured summarization of what features (or aspects) of the product are discussed. For each aspect, we further want to know when people are satisfied or unsatisfied, what they will say. This is the aspect and sentiment extraction task. We also want to know for each review, the sentiment is positive or negative. This is the sentiment classification task. Compared to existing work, our key advantage is that we can figure out how sentiments are expressed according to different polarities for a particular aspect. This is what “aspect-specific polarity-aware” means.
  • Our work can be classified in the framework of aspect-based sentiment classification. Here I give a review of the related work. Generally speaking, there are three kinds of methods. The first one is frequency-based methods, which use frequent pattern mining and dependency parser to find frequent noun terms and opinions cast on them. The limitation is that the produce many non-aspects. The second kind of method is sequential labeling, they employ POS and lexical features on labeled training data to train a CRF or HMM model. However, labelled training data is hard to obtain. The third method is LDA-based models, which use topic modeling to discover latent topics as aspects and sentiments. This kind of methods are unsupervised, which means we do not need labelled training data. However, most existing LDA-based models cannot extract polarity-aware sentiments for each aspect.
  • Now, I will describe our models. First, I describe APSM and its improved version ME-APSM. Then we show how to inference the models. Last, I will describe how to integrate aspect and sentiment prior to APSM and ME-APSM via asymmetric Dirichlet prior.
  • OK, here is the generative process of APSM. First we generate K aspect models and M sentiment models for each aspect. Both aspect and sentiment model are distribution over words. Given the aspect and sentiment models, our review corpus can be generate as follows:For each review d, draw an aspect distribution θ and K sentiment distribution π. For each sentence in d, we first draw its aspect z from 𝜃𝑧. Given z and 𝜋𝑑, we then draw a sentiment l for sentence s. Each sentence has a proportion of aspect and sentiment ψ. For each word in sentence s, we first draw r from ψ, which indicates whether word is an aspect word or sentiment word. If w is an aspect word, we draw it from the z aspect model; else if w is a sentiment word, we draw it from l sentiment model of aspect z.OK, here is the generative process of APSM. First we generate K aspect models and M sentiment models for each aspect. Both aspect and sentiment model are distribution over words. Given the aspect and sentiment models, our review corpus can be generate as follows: For each review d, draw an aspect distribution θ and K sentiment distribution π. For each sentence in d, we first draw its aspect z from 𝜃_𝑧. Given z and 𝜋_𝑑, we then draw a sentiment l for sentence s. Each sentence has a proportion of aspect and sentiment ψ. For each word in sentence s, we first draw r from ψ, which indicates whether word is an aspect word or sentiment word. If w is an aspect word, we draw it from the z aspect model; else if w is a sentiment word, we draw it from l sentiment model of aspect z.
  • Now, I describe ME-APSM. We observe that aspect and sentiment terms play different syntactic roles in a sentence. Aspect words term to be nouns and noun phrases, while sentiment words term to be adjectives and adverbs. So we train a MaxEnt to distinguish them. The training data are obtained by exploiting a sentiment lexicon. The graphical representation of ME-APSM are shown in the right figure.
  • We use collapsed Gibbs Sampling to inference the model. The sampling formula of z and l is the same for APSM and ME-APSM, which are shown as follows. For APSM, the sampler for r is as follows. For ME-APSM, only the first term changes as exp(…).
  • We can see from Table 1 that both APSM and ME-APSM can extract coherent aspects and aspect-specific sentiments well. For example, “breakfast”, “coffee”, “buffet”, “fruit” and “eggs” are all words related to the aspect meal. They are correctly identified by APSM and ME-APSM. In general, ME-APSM performs better than APSM. For the aspect staff, both APSM and ME-APSM.discover aspect word “staff”, but APSM fails to discover more staff-related words like “waiter” and “waitress”, which are successfully captured by ME-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”.
  • Table 2 gives P@nvalues for ME-LDA, APSM and ME-APSM. It can be seen that APSM and ME-APSM give better results than ME-LDA. ME-APSM further outperforms APSM, which suggests the effectiveness of the MaxEnt component.
  • In this section, we present the results of sentiment classification.The experimental results are shown in Table 3.The performance of unified aspect and sentiment models (ASUM, DS-LDA, APSM and ME-APSM) are better than lexicon based method on both data sets. This is because sentiment polarities are dependent on aspects. The lexicon based method can not capture the aspect information of the sentiment words.APSM and ME-APSM consistently outperform ASUM. ASUM can not separateaspects and sentiments. This suggests that separating aspects and sentiments not only improve the aspect extraction performance, but also improve sentiment classification accuracy.We indicate the models with aspect and sentiment seeds as APSM+ and ME-APSM+.Note that the supervised method needs labeled training data while ME-APSM+ only needs several aspect and sentiment seeds.
  • We also analyze the influence of the number of aspects K. We can see from Fig. 3 that as the number of aspectsincrease, the sentiment classification performance increases. This trend is more evident on the product data set.

Aspect-Specific Polarity-Aware Summarization of Online Reviews Aspect-Specific Polarity-Aware Summarization of Online Reviews Presentation Transcript

  • Aspect-Specific Polarity-Aware Summarization of Online Reviews Gaoyan Ou (ougaoyan@126.com) School of EECS, Peking University 1
  • Outline  Motivation  Related work  The proposed APSM and ME-APSM model  Experiments and results  Conclusion 2
  • Outline  Motivation  Related work  The proposed APSM and ME-APSM model  Experiments and results  Conclusion 3
  • Motivation  A large amount of reviews which contain people’s 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
  • 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
  • Outline  Motivation  Related work  The proposed APSM and ME-APSM model  Experiments and results  Conclusion 6
  • 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
  • Outline  Motivation  Related work  The proposed APSM and ME-APSM model  Experiments and results  Conclusion 8
  • 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
  • APSM model  z l w r θ δα D Nd,s K Sd K K M Graphical representation of APSM model 10
  • x Employing MaxEnt (ME-APSM model)  z l w r θα D Nd,s K Sd K K M Graphical representation of ME-APSM model 11
  • Model inference  APSM ME-APSM 12
  • Incorporating Prior Knowledge we expect that no negative word appears in each aspect’s positive sentiment model positive word will be more likely to appear in each aspect’s 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
  • Outline  Motivation  Related work  The proposed APSM and ME-APSM model  Experiments and results  Conclusion 14
  • Experimental setup  15
  • 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”.
  • 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.
  • 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
  • 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.
  • Outline  Motivation  Related work  The proposed APSM and ME-APSM model  Experiments and results  Conclusion 20
  • 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
  • Reference  Hu, M., Liu, B.: Mining and summarizing customer reviews. In: KDD. (2004) 168–177  Jo, Y., Oh, A.H.: Aspect and sentiment unification model for online review analysis. In: WSDM. (2011) 815–824  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) 56–65  Mukherjee, A., Liu, B.: Aspect extraction through semi-supervised modeling. In:ACL (1). (2012) 339–348  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) 465–472  Jakob, N., Gurevych, I.: Extracting opinion targets in a single and cross-domain setting with conditional random fields. In: EMNLP. (2010) 1035–1045  Choi, Y., Cardie, C.: Hierarchical sequential learning for extracting opinions and their attributes. In: ACL (Short Papers). (2010) 269–274  Denecke, K.: Are sentiwordnet scores suited for multi-domain sentiment classification? In: ICDIM. (2009) 33–38 22
  • Thank you, any question? 23