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  • 1. Web Opinion Mining Marc-Antoine Dupré Alexander Patronas Erhard Dinhobl Ksenija Ivekovic Martin Trenkwalder
  • 2. RoadmapWhat is opinion mining and why?Objects, model and taskWords and phrasesSentiment classificationFeature-based opinion miningOpinion SpamTools on opinion mining
  • 3. Questions:What do users think about a specific product?Which of our customers are unsatisfied? Why?Which product is more popular among users? Answer: Web Opinion Mining
  • 4. Web Opinion MiningFacebook, blogs, … > opinionWikipedia > factOpinions: underlying question “ what do people in America think about Barack Obama?” Mostly in deep webAI algorithm necessaryUseful: market intelligence (better ads)
  • 5. Objects, ModelOpinion holder / object / opinionFeatures of object F = {f1, f2, f3, …} fi ϵ Ffi defined by words or phrases W = {w1, w2, w3, …} Wi ϵ WO is some object (event, person, product, …)“Now the opinion holder is j and comments on a subset of features S j of F of O. Now feature fk ϵ Sj is commented by j by a word or phrase from Wk to determine the feature and a positive, negative or neutral opinion on fk”
  • 6. Task One document – one opinion from one holder Opinion: positive, negative, neutral 3 levels:  Document - class determining  Sentence (one opinion)  sentence type (objective or subjective)  sentence class (neutral, positive, negative)  Feature – determining words and phrases
  • 7. Words and PhrasesWords often context dependent („long“ – long loading time – long battery runtime)3 approaches to get wordlist: Manual approach Corpus-based approach Dictionary-based approach
  • 8. Sentiment ClassificationClassify documents (e.g. reviews) based on overall sentiments expressed by opinion holders Positive, negative or neutral Useful, but doesn’t find what reviewer liked or disliked! A negative sentiment on an object doesn’t mean that opinion holder dislikes everything about object and oppositeNeed to go to sentence level and the feature level
  • 9. Feature-based Opinion MiningObjective: find what reviewers like and dislike Features and componentsThree tasks: Extract object features that have been commented on in each review Determine whether opinions on the feature are positive, negative or neutral Group synonyms and produce summary
  • 10. Different Review FormatsGREAT Camera., Jun 3, 2004Reviewer: jprice174 from Atlanta, Ga. I did a lot of research last year before Ibought this camera... It kinda hurt to leave behindmy beloved nikon 35mm SLR, but I was going toItaly, and I needed something smaller, and digital. The pictures coming out of this camera areamazing. The auto feature takes great picturesmost of the time. And with digital, youre notwasting film if the picture doesnt come out. …….
  • 11. Extracting Object Features1. Part-of-speech tagging:  Features are noun and noun phrases2. Frequent features generation  Association mining to generate candidate features  Feature pruning3. Infrequent feature generation  Opinion words extraction  Finding infrequent features using opinion words
  • 12. Identifying Orientation of OpinionSentenceUsed dominant orientation of opinion words as sentence orientation If positive opinion prevails, the opinion sentence is regarded as a positive and vice versa
  • 13. Feature-based SummaryGREAT Camera., Jun 3, 2004 Feature Based Summary:Reviewer: jprice174 from Atlanta, Ga. Feature1: picture I did a lot of research last year before Positive: 12I bought this camera... It kinda hurt to  The pictures coming out of this camera areleave behind my beloved nikon 35mm amazing.SLR, but I was going to Italy, and I needed  Overall this is a good camera with a really goodsomething smaller, and digital. picture clarity. The pictures coming out of this …camera are amazing. The auto feature Negative: 2takes great pictures most of the time. And  The pictures come out hazy if your hands shakewith digital, youre not wasting film if the even for a moment during the entire process ofpicture doesnt come out. … taking a picture.  Focusing on a display rack about 20 feet away in a brightly lit room during day time, pictures…. produced by this camera were blurry and in a shade of orange. Feature2: battery life …
  • 14. Opinion SpamReviews contain rich user opinions on products and services, that possibly influence the purchase decisions of usersGenerally three types of spam reviews: Untruthful opinions Reviews on brands only Non-Reviews
  • 15. Tools for Sentiment Analysis [1/2]APIs Evri – semantic search engine, very powerful API OpenDover – Java based webserviceBlogosphere/Twittersphere RankSpeed – search by criterias Twittratr – simple search tool (keyword based) TwitterSentiment – project from Stanford University, classifiers from machine learning algorithms, transparent
  • 16. Tools for Sentiment Analysis [2/2]Newspaper Newssift – sentiment search tool on newspapers (by Financial Times)Applications LingPipe – Java tool Radian6 – commercial social media monitoring application RapidMiner – open-source machine learning and data mining tool (Community Edition)
  • 17. LIVE DEMO (evri)
  • 18. Thank youfor your attention!