1. 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. 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. 3 An Illustrative Example Should I buy an iPod? Thumb up or thumb down? Positive, negative, neutral… (Sentiments) Are their opinions changing? Negative before 2005, but positive recently… (Dynamics) What do people say about ipod? Price, battery, warranty, nano, … (Topics) What aspects are good/bad? Sound is good, battery is bad.. (Faceted opinions)

4. 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. 5 Evidence from Blog Search availability Broad distribution Positive: …the trail leads to fascinating places that are richly … Negative: …when I first watched the big-screen version of The Da Vinci Code, I fell asleep twice. Not once. Twice! … Opinion rich Topic diversity

6. 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. 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] No sentiment analysis, no faceted analysis: what if the hot discussion is “Negative”? Hot criticisms may not lead to sales spikes [from Gruhl et al. 2005]

8. 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. 9 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 time strength Positive Negative Topic-sentiment dynamics (Topic = Price ) Neutral Query: Dell Laptop Topic-sentiment summary positivenegative Topic 2 (Battery) Topic 1 (Price) neutral my Dell battery sucks Stupid Dell laptop battery One thing I really like about this Dell battery is the Express Charge feature. i still want a free battery from dell.. …… it is the best site and they show Dell coupon code as early as possible Even though Dell's price is cheaper, we still don't want it. …… mac pro vs. dell precision: a price comparis.. DELL is trading at $24.66

10. 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. 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. 12 Probabilistic Model of Topic-Sentiment Mixture kk 11 22 B Facet  1 Facet  k Facet  2 … Background B Choose a facet (subtopic)  i battery 0.3 life 0.2.. nano 0.1 release 0.05 screen 0.02.. apple 0.2 microsoft 0.1 compete 0.05.. Is 0.05 the 0.04 a 0.03.. … love 0.2 awesome 0.05 good 0.01.. suck 0.07 hate 0.06 stupid 0.02.. P N P F NP F N P F N battery love hate the Draw a word from the mixture of topics and sentiments ( ) FPN

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

14. 14 Count of word w in document d The Likelihood Function Generating w using the background model Choosing a faceted opinion Generating w using the neutral topic model Generating w using the positive sentiment model Generating w using the negative sentiment model

15. 15 Two Modes for Parameter Estimation Training Mode: Learn the sentiment model Testing Mode: Extract the Topic models Fixed for each d Feed strong prior on sentiment models One of them is zero for d

16. 16 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

17. 17 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

18. 18 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) E T3T2 11 P N B T1 From and to E … the battery really sucks and it's really heavy in my part but where could you find laptops so affordable nowadays?...

19. 19 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) Datasets# docsTime PeriodQuery Term iPod298801/06 ~ 11/06ipod Da Vinci Code100001/06 ~ 10/06da+vinci+code Topic# Pos# NegTopic# Pos# Neg laptops346142people441475 movies396398banks292229 universities464414insurances354297 airlines283400nba teams262191 cities500 cars399334

20. 20 Results: General Sentiment Models Sentiment models trained from diversified topic mixture v.s. single topics Pos-CitiesNeg-CitiesPos-MixNeg-Mix beautifulhatelovesuck lovesuckawesomehate awesomepeoplegoodstupid amazetrafficmissass livedriveamazefuck goodfuckprettyhorrible nightstinkjobshitty nicemovegodcrappy timeweatheryeahterrible aircityblesspeople greatesttransportexcellentevil # topic mixture in training data KL Divergence between learnt  p and  N and unseen topic

21. 21 Results: Facets and Topic Models (I) Facets for iPod : No PriorWith Prior Battery, nanoMarketingAds, spamNanoBattery batteryapplefreenanobattery shufflemicrosoftsigncolorshuffle chargemarketofferthincharge nanozunefreepayholdusb dockdevicecompletemodelhour itunecompanyvirus4gbmini usbconsumerfreeipoddocklife hoursaletrialinchrechargable

22. 22 Results: Facets and Topic Models (II) Facets for the Da Vinci Code No PriorWith Prior StoryBookBackgroundMovieReligion landonauthorjesusmoviereligion secretideamaryhankbelief murderholygospeltomcardinal louvrecourtmagdalenefilmfashion thrillbrowntestamentwatchconflict cluebloodgnostichowardmetaphor neveucopyrightconstantineroncomplaint curatorpublishbibleactorcommunism

23. 23 Results: Faceted Opinions (the Da Vinci Code) NeutralPositiveNegative Facet 1: Movie... Ron Howards selection of Tom Hanks to play Robert Langdon. Tom Hanks stars in the movie,who can be mad at that? But the movie might get delayed, and even killed off if he loses. Directed by: Ron Howard Writing credits: Akiva Goldsman... Tom Hanks, who is my favorite movie star act the leading role. protesting... will lose your faith by... watching the movie. After watching the movie I went online and some research on... Anybody is interested in it?... so sick of people making such a big deal about a FICTION book and movie. Facet 2: Book I remembered when i first read the book, I finished the book in two days. Awesome book.... so sick of people making such a big deal about a FICTION book and movie. I’m reading “Da Vinci Code” now. … So still a good book to past time. This controversy book cause lots conflict in west society.

24. 24 Results: Comparison with Opinmind Faceted opinions from TSM FacetsThumbs UpThumbs Down iPod Nano(sweat) iPod Nano ok so... Ipod Nano is a cool design,... WHAT IS THIS SHIT??!! ipod nanos are TOO small!!!! Batterythe battery is one serious example of excellent relibability Poor battery life......iPod’s battery completely died iPod VideoMy new VIDEO ipod arrived!!! Oh yeah! New iPod video fake video ipod Watch video podcasts... Opinions from Opinmind: Thumbs UpThumbs Down I love my iPod, I love my G5...I hate ipod. I love my little black 60GB iPodStupid ipod out of batteries... I LOVE MY iPOD“ hate ipod ” = 489.. I love my iPod.my iPod looked uglier...surface... - I love my iPod.i hate my ipod.... iPod video looks SO awesome... microsoft... the iPod sucks

25. 25 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 )

26. 26 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

27. 27 Thank You!