Topic-Sentiment Mixture: Modeling Facets and Opinions in Weblogs

1. Topic-Sentiment Mixture: Modeling Facets and Opinions in Weblogs Qiaozhu Mei†, Xu Ling†, Matthew Wondra†, Hang Su‡, and ChengXiang Zhai† † University of Illinois at Urbana-Champaign ‡ Yahoo! Inc.

2. Why Opinion Analysis? • Customers: need peer opinions to make purchase decisions • Business providers: • need customers’ opinions to improve product • need to track opinions to make marketing decisions • Social researchers: want to know people’s reactions about social events • Government: wants to know people’s reactions to a new policy • Psychology, education, etc.

3. What do people say about ipod? • Price, battery, warranty, nano, … (Topics) • What aspects are good/bad? • Sound is good, battery is bad.. • (Facetedopinions) • Thumb up or thumb down? • Positive, negative, neutral… (Sentiments) • Are their opinions changing? • Negative before 2005, but positive • recently… (Dynamics) An Illustrative Example Should I buy an iPod?

4. Why Extracting Opinions from Blogs? • Easy to collect: huge amount, clean format • Broadly distributed: demographics • Topic diversified: free discussion about any topic/product/event • Opinion rich: highly personalized

5. Topic diversity availability Broad distribution Positive: …the trail leads to fascinating places that are richly … Opinion rich Negative: …when I first watched the big-screen version of The Da Vinci Code, I fell asleep twice. Not once. Twice! … Evidence from Blog Search

6. Existing Blog-opinion Analysis Work • Opinmind: sentiment classification/search of blogs No faceted analysis, no neutral fact description: Not informative enough to support decision making

7. Existing Blog-opinion Analysis Work (Cont.) • Use content to predict sales • Blog level topic analysis • Information Diffusion through blogspace • Use topic bursting to predict sales spikes • E.g., [Gruhl et al. 2005] [from Gruhl et al. 2005] No sentiment analysis, no faceted analysis: what if the hot discussion is “Negative”? Hot criticisms may not lead to sales spikes

8. What’s Missing Here? • Discussions are faceted • E.g. iPod: battery? Price? Nano? … • Usually different opinions on different facets • Opinions have polarities • Positive, negative, and neutral … • Non-discriminative analysis may lead to wrong decision • Opinions are changing over time …

9. Topic-sentiment dynamics (Topic = Price) strength Positive Negative Neutral Topic-sentiment summary Query: Dell Laptop time positive negative neutral • Even though Dell's price is cheaper, we still don't want it. • it is the best site and they show Dell coupon code as early as possible • mac pro vs. dell precision: a price comparis.. Topic 1 (Price) • DELL is trading at $24.66 • …… • my Dell battery sucks • One thing I really like about this Dell battery is the Express Charge feature. • i still want a free battery from dell.. Topic 2 (Battery) • Stupid Dell laptop battery • …… • …… Our Goal • Model the mixture of facets and opinions (topics and sentiments) • Generate a faceted opinion summarization for ad hoc query • Track the change of opinions over time

10. Challenges in Opinion Analysis from Blogs • Topics and sentiments are mixed together • No existing facet structure for ad hoc topics • Difficult to identify sentiment polarities • Difficult to associate sentiment polarities with facets • Difficult to segment topics and sentiments • Tracking sentiment dynamics

11. Our Approach: Modeling Topic-Sentiment Mixture • Use language models to represent facets and sentiments • Facets represented with topic models, extracted in an unsupervised/semi-supervised way • Sentiment models extracted in a supervised way • Model the mixture of topics and sentiments with a probabilistic generative model • Segment associated topics and sentiments with a topical hidden Markov model

12. Choose a facet (subtopic) i Draw a word from the mixture of topics and sentiments ( ) battery F P N battery 0.3 life 0.2.. F Facet 1 love P N nano 0.1release 0.05screen 0.02 .. Facet 2 1 F 2 P N … apple 0.2microsoft 0.1compete 0.05 .. F hate k Facet k P N B Is 0.05the 0.04a 0.03 .. the Background B love 0.2awesome 0.05good 0.01 .. suck 0.07hate 0.06stupid 0.02 .. N P Probabilistic Model of Topic-Sentiment Mixture …

13. 1 1, d, F Neutral, Facts 2 1 2, d, F d1 … 1 - B Topics 2 k, d, F w d2 k … j, d, P dk k Positive P B d Negative B j, d, N N The “Generation” Process p(w| T) p(w| i ) • p(w|i), p(w| p), p(w| N) can be estimated with Maximum Likelihood Estimator (MLE) through an EM algorithm

14. Learning Sentiment Models • Problem: Sentiment expressions are topic-biased • E.g., “fearful” is negative in general , but how about for a ghost movie? • E.g., “heavy” is positive for rock music, but how about for laptops? • Impossible to create training data for every ad hoc topic • Solution: • Collect sentiment labeled data with diversified topics • Learn a general sentiment model from the mixed training data in training mode • Use this general sentiment model as prior, get the topic-biased sentiment models in testing mode

15. Estimating Topic Models • Problem: no existing facet structure for ad hoc topics • Unsupervised extraction: facets might not be what you like • E.g., user wants “battery”, “price” and “sound quality” • System returns “ipod nano”, “ipod video”, “ipod shuffle”.. • Solution: Incorporate user specified interests into automatically extracted facets • User provides hints; add priors into the topic model • Using MAP estimation instead of MLE • See paper for technical details

16. B P N 1 T1 E From and to E T2 T3 Sentiment Segmentation and Dynamics Tracking • Design a topic-sentiment enhanced HMM • Associate states with topic/sentiment models • Learn the transition prob. and segment the text • Plot the sentiment dynamics by counting segments over time ( tagged with each facet and sentiment) … the battery really sucks and it's really heavy in my part but where could you find laptops so affordable nowadays?...

17. Experiment Setup • Training data for sentiment models (diversified topics, downloaded from Opinmind) • Test dataset: created by querying Google blog search and crawling from original sites (ad hoc)

18. Results: General Sentiment Models • Sentiment models trained from diversified topic mixture v.s. single topics KL Divergence between learnt p and N and unseen topic # topic mixture in training data

19. Results: Facets and Topic Models (I) • Facets for iPod :

20. Results: Facets and Topic Models (II) • Facets for the Da Vinci Code

21. Results: Faceted Opinions(the Da Vinci Code)

22. Results: Comparison with Opinmind • Faceted opinions from TSM Opinions from Opinmind:

23. Results: Sentiment Dynamics Facet: the book “ the da vinci code”. ( Bursts during the movie, Pos > Neg ) Facet: the impact on religious beliefs. ( Bursts during the movie, Neg > Pos )

24. Summary and Future Work • Algorithm: A new way to model the mixture of topics and sentiments • Application: A new way to summarize faceted opinions, and track their dynamics • Future Work: • Beyond unigram language model? • Better segmentation of sentiments and topics? • Adapting existing facet structures? • Develop an end user application for opinion analysis

25. Thank You!