Topic-Sentiment Mixture: Modeling Facets and Opinions in Weblogs Qiaozhu Mei†, Xu Ling†, Matthew Wondra†, Hang Su‡, and Cheng. Xiang Zhai† † University of Illinois at Urbana-Champaign ‡ Yahoo! Inc. 1
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. 2
An Illustrative Example Should I buy an i. Pod? • What do people say about ipod? Price, battery, warranty, nano, … (Topics) • Thumb up or thumb down? Positive, negative, neutral… (Sentiments) • What aspects are good/bad? Sound is good, battery is bad. . (Faceted opinions) • Are their opinions changing? Negative before 2005, but positive recently… (Dynamics) 3
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 4
Evidence from Blog Search Topic diversity availability Broad distribution Positive: …the trail leads to Opinion rich 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! … 5
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 6
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 7
What’s Missing Here? • Discussions are faceted – E. g. i. Pod: 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 … 8
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 Topic-sentiment summary Query: Dell Laptop positive Topic 1 (Price) Topic 2 (Battery) negative neutral • it is the best • Even though • mac pro vs. site and they show Dell coupon code as early as possible Dell's price is cheaper, we still don't want it. dell precision: a price comparis. . • …… • my Dell battery strength Positive Negative at $24. 66 • One thing I Topic-sentiment dynamics (Topic = Price) • i still want a really like about this Dell battery is the Express Charge feature. sucks • Stupid Dell laptop battery • …… Neutral • DELL is trading free battery from dell. . • …… time 9
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 10
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 11
Probabilistic Model of Topic-Sentiment Mixture Choose a facet (subtopic) i Draw a word from the mixture of topics and sentiments ( F P N ) Facet 1 Facet 2 … Facet k Background B battery 0. 3 life 0. 2. . nano 0. 1 release 0. 05 screen 0. 02. . battery F P N F apple 0. 2 microsoft 0. 1 F compete 0. 05. . 1 love P 2 N … k hate P N Is 0. 05 the 0. 04 a 0. 03. . B the P love 0. 2 awesome 0. 05 good 0. 01. . N suck 0. 07 hate 0. 06 stupid 0. 02. . 12
The “Generation” Process 1 2, d, F … k k, d, F j, d, P 1 2 … Positive Negative k P d 1 1 - B d 2 B p(w| i ) w dk B j, d, N N p(w| T ) Topics Neutral, Facts 2 1, d, F d • p(w| i), p(w| p), p(w| N) can be estimated with Maximum Likelihood Estimator (MLE) through an EM algorithm 13
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 16
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 17
Sentiment Segmentation and Dynamics Tracking • Design a topic-sentiment enhanced HMM P N • Associate states with T 1 topic/sentiment models E • Learn the transition prob. From and T 2 T 3 and segment the text to E • Plot the sentiment … the battery really sucks and dynamics by counting it's really heavy in my part segments over time ( but where could you find laptops tagged with each facet so affordable nowadays? . . . and sentiment) B 1 18
Experiment Setup • Training data for sentiment models (diversified topics, downloaded from Opinmind) Topic # Pos # Neg laptops 346 142 people 441 475 movies 396 398 banks 292 229 universities 464 414 insurances 354 297 airlines 283 400 nba teams 262 191 cities 500 cars 399 334 • Test dataset: created by querying Google blog search and crawling from original sites (ad hoc) Datasets # docs Time Period Query Term i. Pod 2988 01/06 ~ 11/06 ipod Da Vinci Code 1000 01/06 ~ 10/06 da+vinci+code 19
Results: General Sentiment Models • Sentiment models trained from diversified topic mixture v. s. single topics Pos-Cities Neg-Cities Pos-Mix Neg-Mix beautiful hate love suck awesome hate awesome people good stupid amaze traffic miss ass live drive amaze fuck good fuck pretty horrible night stink job shitty nice move god crappy time weather yeah terrible air city bless people greatest transport excellent evil KL Divergence between learnt p and N and unseen topic # topic mixture in training data 20
Results: Facets and Topic Models (I) • Facets for i. Pod : No Prior With Prior Battery, nano Marketing Ads, spam Nano Battery battery apple free nano battery shuffle microsoft sign color shuffle charge market offer thin charge nano zune freepay hold usb dock device complete model hour itune company virus 4 gb mini usb consumer freeipod dock life hour sale trial inch rechargable 21
Results: Facets and Topic Models (II) • Facets for the Da Vinci Code No Prior With Prior Story Book Background Movie Religion landon author jesus movie religion secret idea mary hank belief murder holy gospel tom cardinal louvre court magdalene film fashion thrill brown testament watch conflict clue blood gnostic howard metaphor neveu copyright constantine ron complaint curator publish bible actor communism 22
Results: Faceted Opinions (the Da Vinci Code) Neutral 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. . . Facet 2: Book Negative . . . Ron Howards selection of Tom Hanks to play Robert Langdon. Facet 1: Movie Positive Anybody is interested in it? . . . so sick of people making such a big deal about a FICTION book and movie. 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. 23
Results: Comparison with Opinmind • Faceted opinions from TSM Facets Thumbs Up Thumbs Down i. Pod Nano (sweat) i. Pod Nano ok so. . . Ipod Nano is a cool design, . . . WHAT IS THIS SHIT? ? !! ipod nanos are TOO small!!!! Battery the battery is one serious example of excellent relibability Poor battery life. . . i. Pod’s battery completely died i. Pod Video My new VIDEO ipod arrived!!! Oh yeah! New i. Pod video fake video ipod Watch video podcasts. . . Thumbs Up Opinions from Opinmind: Thumbs Down I love my i. Pod, I love my G 5. . . I hate ipod. I love my little black 60 GB i. Pod Stupid ipod out of batteries. . . I LOVE MY i. POD “ hate ipod ” = 489. . I love my i. Pod looked uglier. . . surface. . . - I love my i. Pod. i hate my ipod. . i. Pod video looks SO awesome . . . microsoft. . . the i. Pod sucks 24
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 ) 25
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 26
Thank You! 27
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