SentiCircles for Contextual and Conceptual Semantic Sentiment Analysis of Twitter
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The document presents a novel approach called 'senticircles' for semantic sentiment analysis on Twitter, which combines contextual and conceptual semantics to enhance sentiment detection. It discusses the limitations of traditional lexicon-based methods and machine learning approaches and demonstrates how senticircles outperformed existing models, including the state-of-the-art 'sentistrength.' The study shows the effectiveness of senticircles in accurately analyzing tweet-level sentiment and proposes further research in dynamic sentiment adaptation.
![Capturing Contextual Semantics
Term (m) C1 C2 Cn….
Context-Term Vector
Degree of Correlation
Prior SentimentSentiment
Lexicon
(1)
(2)
Great
Smile Look
(3)
Contextual Sentiment
Strength
Contextual Sentiment
Orientation
Positive,
Negative
Neutral
[-1 (very negative)
+1 (very positive)]](https://image.slidesharecdn.com/patternsslidesewscslideshare-140528040101-phpapp02/85/SentiCircles-for-Contextual-and-Conceptual-Semantic-Sentiment-Analysis-of-Twitter-17-320.jpg)
![Experimental Setup
(1) Datasets
(2) Sentiment Lexicons
- SentiWordNet [3]
- MPQA Subjectivity Lexicon [4]
- Thelwall-Lexicon [5]](https://image.slidesharecdn.com/patternsslidesewscslideshare-140528040101-phpapp02/85/SentiCircles-for-Contextual-and-Conceptual-Semantic-Sentiment-Analysis-of-Twitter-28-320.jpg)
SentiCircles for Contextual and Conceptual Semantic Sentiment Analysis of Twitter
- 1. SentiCircles for Contextual and Conceptual Semantic Sentiment Analysis of Twitter Hassan Saif, Miriam Fernandez, Yulan He and Harith Alani The Eleventh Extended Semantic Web Conference (ESWC2014) May 2014
- 3. “Sentiment analysis is the task of identifying positive and negative opinions, emotions and evaluations in text” 3 Opinion OpinionFact Sentiment Analysis yes, It is sunny, but also very humid :( The weather is great today :) I think its almost 30 degrees today Sentiment Analysis
- 8. Lexicon-based Approach I had nightmares all night long last night :( Negative Sentiment Lexicon Text Processing Algorithm great sad down wrong horrible love
- 9. o Requires Labeled Twitter Corpora Labor Intensive Task Distant Supervision (Noisy Labeling) o Domain Specific Re-Training with new domains Machine Learning Approach On Twitter?
- 10. Traditional Lexicons - MPQA & SentiwordNet, etc - Not tailored to Twitter noisy data: - lol, gr8, wow, :), :P - Fixed number of words Lexicon-based Approach On Twitter? Sentiment Lexicon great sad down wrong horrible love grt8lol :) :P
- 11. Twitter-specific Lexicon-based Methods - Such as SentiStrength - Rule-base method for sentiment analysis on social web - Uses Thelwall-Lexicon - Built to specifically work on social data - Contain lists of emoticons, slangs, abbreviations, etc.
- 12. • Fixed Number of words • Offer Context-Insensitive Prior Sentiment Orientations and Strength of words Great Problem Smile Positive Thelwall-Lexicon & SentiStrength Sentiment Lexicon great sad down wrong horrible love
- 13. We Need.. Unsupervised Approach Understands the Semantic of Words Captures their Contexts Updates Sentiment
- 14. SentiCircles
- 15. SentiCircles Lexicon-based Approach Builds Dynamic representation of words Captures Contextual & Conceptual Semantics of words Updates words’ sentiment orientation and strength accordingly
- 16. Contextual Semantics “Words that occur in similar context tend to have similar meaning” Wittgenstein (1953) “You Shall know the word by the company it keeps” Firth (1955) Great Problem Look Smile Concert Song Weather Loss Game Taylor Swift Amazing Great
- 17. Capturing Contextual Semantics Term (m) C1 C2 Cn…. Context-Term Vector Degree of Correlation Prior SentimentSentiment Lexicon (1) (2) Great Smile Look (3) Contextual Sentiment Strength Contextual Sentiment Orientation Positive, Negative Neutral [-1 (very negative) +1 (very positive)]
- 18. Term (m) C1 Degree of Correlation Prior Sentiment Great Smile SentiCircles Model X = R * COS(θ) Y = R * SIN(θ) Smile X ri θi xi yi Great PositiveVery Positive Very Negative Negative +1 -1 +1-1 Neutral Region ri = TDOC(Ci) θi = Prior_Sentiment (Ci) * π Capturing Contextual Semantics
- 20. Overall Contextual Sentiment Ci X ri θi xi yi m PositiveVery Positive Very Negative Negative +1 -1 +1-1 Neutral Region nwhicheachtermisused. Tocomputethenewsentiment of tiCircleweusetheSenti-Median metric. Wenow havethe hichiscomposedbytheset of (x, y) Cartesiancoordinatesof wherethey valuerepresentsthesentiment andthex value ength. Aneffectiveway toapproximatetheoverall sentiment y calculatingthegeometricmedianof all itspoints. Formally, (p1, p2, ..., pn ) inaSentiCircle⌦, the2Dgeometricmedian g = arg min g2 R2 nX i = 1 k|pi − g||2, (5) Senti-Median of SentiCircle Sentiment Function
- 21. SentiCircles & Conceptual Semantics
- 22. Enriching SentiCircles with Conceptual Semantics Sushi time for fabulous Jesse's last day on dragons den @Stace_meister Ya, I have Rugby in an hour Dear eBay, if I win I owe you a total 580.63 bye paycheck Company Person Sport
- 23. Enriching SentiCircles with Conceptual Semantics Cycling under a heavy rain.. What a #luck! Weather Condition Wind Snow Humidity
- 24. SentiCircles for Tweet-level Sentiment Analysis Detecting the overall Sentiment of a given tweet message (positive vs. negative)
- 25. SentiCircles for Tweet-level Sentiment Analysis (1) The Median Method Cycling under a heavy rain.. what a #luck! S-Median S-Median S-Median S-Median S-Median S-Median The Median of Senti-Medians
- 26. Tweet-level Sentiment Analysis (2) The Pivot Method like1 X Y r1 θ1 PositiveVery Positive Very Negative Negative new2 pj r2 θ2 like1 new2 iPadj Wn Sj1 Sj2 Tweet tk ... et termstobeequal. Eachtweet ti 2 T isturnedintoavector of Senti- 1, g2, ..., gn ) of sizen, wheren isthenumber of termsthat composethe heSenti-Medianof theSentiCircleassociatedwithtermmj . Equation culatethemedianpoint q of g, whichweusetodeterminetheoverall eet ti using Function 6. od: Thismethodfavourssometermsinatweet over others, basedon that sentiment isoftenexpressedtowardsoneor morespecifictargets, toas“Pi vot ” terms. Inthetweet exampleabove, therearetwopivot e” and“i Pad” sincethesentiment word“amazi ng” isusedtodescribe ence, themethodworksby(1) extractingall pivot termsinatweet and; g, for eachsentiment label, thesentiment impact that eachpivot term her terms. Theoverall sentiment of atweet correspondstothesentiment ighest sentiment impact. Opiniontarget identificationisachallenging ondthescopeof our current study. For simplicity, weassumethat the hosehavingthePOStags: {CommonNoun, Proper Noun, Pronoun} in hcandidatepivot term, webuildaSentiCirclefromwhichthesentiment vot termreceivesfromall theother termsinatweet canbecomputed. vot-Methodseekstofindthesentiment ˆs that receivesthemaximum ct within atweet as: ˆs = argmax s2 S Hs(p) = argmax s2 S N p X i N wX j Hs(pi , wj ) (7) I like my new iPad
- 27. Experiments
- 28. Experimental Setup (1) Datasets (2) Sentiment Lexicons - SentiWordNet [3] - MPQA Subjectivity Lexicon [4] - Thelwall-Lexicon [5]
- 29. Experimental Setup (3) Baselines 1. Lexicon-Labeling (MPQA & SentiWordNet) Average of positive & negative words in a tweet. 2. SentiStrength (State-of-the-art) - Lexicon-based method built for Twitter - Apply a set of syntactic rules
- 30. Results
- 31. Sentiment Detection with Contextual Semantics
- 32. SentiCircles vs. Lexicon-Labeling Methods 52.35 52.74 74.96 52.34 52.30 68.06 40.00 45.00 50.00 55.00 60.00 65.00 70.00 75.00 80.00 MPQA-Lex SentiWNet-Lex SentiCircle Accuracy F-Measure
- 33. SentiCircle vs. SentiStrength Datasets Accuracy F1 OMD SentiCircle SentiCircle HCR SentiCircle SentiStrength STS-Gold SentiStrength SentiStrength Average SentiCircle SentiStrength
- 34. Why Such Variance.. • The sentiment class distribution in our datasets – SentiCircle produces, on average, 2.5% lower recall than SentiStrength on positive tweet detection – Our datasets contain more negative tweets than positive ones • Topic Distribution in the three datasets • More research is required
- 35. Sentiment Detection with Conceptual Semantics Win/Loss in Accuracy and F-measure of incorporating conceptual semantics into SentiCircles, where Mdn: SentiCircle with Median method, Pvt: SentiCircle with Pivot method.
- 36. Conclusion • We proposed a novel semantic sentiment approach called SentiCircle • SentiCircles captures context and update sentiment accordingly • We showed how SentiCircle can be applied for Tweet-level sentiment analysis • SentiCircles outperformed other lexicon labeling methods and overtake the state-of-the-art SentiStrength approach in accuracy, with a marginal drop in F-measure.
- 37. SentiCircles for Sentiment Analysis 1. Tweet-level Sentiment Analysis 1. Entity-Level Sentiment Analysis 2. Sentiment Lexicon Adaptation 3. Dynamic Stopwords Generation 4. Sentiment Patterns Discovery Saif et al. (2014) at ESWC Conference. Greece, Crete Saif et al. (2014), IPM Journal Saif et al. (2014) at ESWC Conference Saif et al. (2014) at LREC Conference. Reykjavik, Iceland Saif et al. (2014) submitted to ISWC Conference.
- 38. Thank You Email: hassan.saif@open.ac.uk Twitter: hrsaif Website: tweenator.com
Editor's Notes
- #18: The workflow in our approach starts with capturing the contextual semantics of words where the first we compute the semantics of a term m by considering the relations of m with all its context words (i.e., words that occur with m in the same context). To compute the individual relation between the term m and a context term ci we propose the use of the Term Degree of Correlation (TDOC) metric. Inspired by the TF-IDF weighting scheme this metric is computed as: Now need to encode these information in away that enable us to measure the sentiment orientations and strength separately. Harith suggested to add a slide about the transition to SentiCircles