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Can Deep Learning solve the Sentiment Analysis Problem

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Mark Cieliebak
Mark Cieliebak

Sentiment analysis appears to be one of the easier tasks in the realm of text analytics: given a text like a tweet or product review, decide whether it contains positive or negative opinion. This task is almost trivial for humans, but it turns out to be a true challenge for automated systems. In fact, state-of-the-art sentiment analysis tools are wrong on approx. 4 out of 10 documents. Current sentiment analysis tools are rule-based, feature-based, or combinations of both. However, recent research uses deep learning on very large sets of documents. In this talk, we will explain the intrinsic difficulties of automated sentiment analysis; present existing solution approaches and their performance; describe an architecture for a deep learning system; and explore whether deep learning can improve sentiment analysis accuracy.

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Can Deep Learning solve the Sentiment Analysis Problem? Mark CieliebakZurichUniversity ofApplied Sciences Annual Meeting ofSGAICO –Swiss Group forArtificialIntelligenceandCognitiveScience 18.11.2014
Outline 1.What is sentiment analysis? 2.How good are "classical" approaches? 3.Does deep learning solve the problem? 18.11.2014 Mark Cieliebak 2
About Me 18.11.2014 Mark Cieliebak 3 Mark Cieliebak Institute of Applied Information Technology (InIT) ZHAW, Winterthur Email: ciel@zhaw.ch, Website: www.zhaw.ch/~ciel Text Analytics Open Data Automated Test Generation Research Interests Software Engineering
WhatisSentiment Analysis "… WiFiAnalytics isa freeAndroid appthatI find veryhandywhenitcomestotroubleshootingandmonitoringa homenetwork. "[1] 18.11.2014 Mark Cieliebak 4
Sample Application: SocialMedia Monitoring Text AnalyticsComponents: •Find relevant documents •Hot topicAnalysis •Sentiment analysis 18.11.2014 Mark Cieliebak 5 [7]
FlavoursofSentiment Analysis •DocumentBased •SentenceBased •Target-Specific •Rating Prediction 18.11.2014 Mark Cieliebak 6
Classic ApproachestoSentiment Analysis Rule-Based Corpus-Based 18.11.2014 Mark Cieliebak 7 Predicted Label [3] [4]
Simple Sentiment Analysis Idea: Count numberofpositive andnegative words "This cameraisgreat[+1]." +1 (pos) "I find itbeautiful[+1]andgood[+1]." +2 (pos) "Itlooksterrible[-1]." -1 (neg) "This carhasa bluecolor." 0 (neu) POSITIVE: great love nice ... NEUTRAL: hello see I … NEGATIVE: bad hate ugly ... UseSentiment-Dictionary: 18.11.2014 Mark Cieliebak 8
Sample Rules 18.11.2014 Mark Cieliebak 9 •DetectBooster Words: "The carisreallyveryexpensive[-1 -1 -2]." •New Category"Mixed": "This carhasan appealing[+1]design andcomfortable[+1]seats, but itisexpensive[-1]." •Negation: Invertonlyscore ofwordsoccuringafter thenegation: "The carisappealing[+3]andI do not[*-1]find itexpensive[-2]" •I do notfind thecarexpensiveanditisappealing. Need to“understand” thesentence
Linguistic Analysis -> RULE: Invertscoresofwordsbeingin thesame phrasesasnegation. “I do not find thecarexpensive[+2] anditisappealing[+3].” → +5 (pos) Sentence Sentence Conj. Sentence NounPhrase Verb Phrase Verb Adverb Verb Noun Phrase Adj. Noun Phrase Verb Phrase Det. Det Noun Det. Verb Participle I do not find the car expensive and it is appealing 18.11.2014 Mark Cieliebak 10
Rule-BasedSentiment Analysis Most ImportantIssues: -Requiresgoodhand-craftedrules -Hard totransfertonewtasksorlanguages -Doesnot workwellfortextswithbadgrammer(Twitter) 18.11.2014 Mark Cieliebak 11 [5]
Classic ApproachestoSentiment Analysis Rule-Based Corpus-Based 18.11.2014 Mark Cieliebak 12 Predicted Label [3] [4]
Corpus-BasedSentiment Analysis 18.11.2014 Mark Cieliebak 13 Predicted Label [4]
Corpus-BasedSentiment Analysis AnnotatedCorpus Sentence Polarity This analysis is good. Pos It looks awful. Neg This car has a blue color. Neu This car has an appealing design, comfortable seats, but it is expensive. Mix This carhasa veryappealingdesign, comfortableseats, but itisreallyexpensive. Mix This analysis is not good. Neg This car has an appealing design, comfortable seats and it is not expensive. Mix This movie was like a horror event. Neg This carisappealingandisnot expensive. Mix ... ... 18.11.2014 Mark Cieliebak 14
Sample Features forTweets •Word ngrams:presence or absence of contiguous sequences of 1, 2, 3, and 4 tokens; noncontiguous ngrams •POS: the number of occurrences of each part-of-speechtag •SentimentLexica: eachwordannotatedwithtonalityscore (-1..0..+1) •Negation: the number of negated contexts •Punctuation: the number of contiguous sequences of exclamation marks, question marks, and both exclamation and question marks •Emoticons: presenceorabsence, last token is a positive or negative emoticon; •Hashtags: the number of hashtags; •Elongatedwords: the number of words with one character repeated (e.g. ‘soooo’) from: Mohammad et al., SemEval2013 18.11.2014 Mark Cieliebak 15
Corpus-BasedSentiment Analysis Most ImportantIssues: -Requireslarge annotatedcorpora -Dependson goodfeatures 18.11.2014 Mark Cieliebak 16 [6]
HowgoodareSentiment Analysis Tools? 18.11.2014 Mark Cieliebak 17
Quick Poll •Short texts: 1-2 sentencesfromTwitter, news, reviewsetc. •Three-classclassification: positive, negative, other •Accuracy= #푐표푟푟푒푐푡푑표푐푠 #푑표푐푠 Mark Cieliebak 21 Accuracy Votes <50% 50-60% 60-70% 70-80% 80-90% >90% "Howgoodarestate-of-the-art sentimentanalysistools?" 18.11.2014
Tool Accuracy 0,2 0,3 0,4 0,5 0,6 0,7 0,8 Accuracy Best Tool per Corpus Worst Tool per Corpus 22 61% 40% Avg. 18.11.2014 Mark Cieliebak [14]
Tool Accuracy 0,2 0,3 0,4 0,5 0,6 0,7 0,8 Accuracy Best Tool per Corpus Worst Tool per Corpus Overall Best Tool 23 61% 40% 59% Avg. 18.11.2014 Mark Cieliebak
Take-Home Lesson Accuracyofbestcommercialtoolon arbitraryshorttextsis59% 18.11.2014 Mark Cieliebak 24
ApproachestoSentiment Analysis Rule-Based Corpus-Based 18.11.2014 Mark Cieliebak 25 Predicted Label [9] DeepLearning [8]
DeepLearning on Text It'sall aboutWord Vectors! 18.11.2014 Mark Cieliebak 26
Word2Vec •Hugesetoftextsamples(billionsofwords) •Extractdictionary •Word-Matrix: k-dimensional vectorforeachword(k typically50-500) •Word vectorinitializedrandomly •Train wordvectorstopredictnextwords, givena sequenceofwordsfromsample text 18.11.2014 Mark Cieliebak 27 Major contributionsbyBengioet al. 2003, Collobert&Weston2008, Socher et al. 2011, Mikolovet al. 2013 [9]
The Magic ofWord Vectors 18.11.2014 Mark Cieliebak 28 King -Man + Woman≈ Queen Live Demo on 100b wordsfromGoogle News dataset: http://radimrehurek.com/2014/02/word2vec-tutorial/ [10]
Relations LearnedbyWord2Vec 18.11.2014 Mark Cieliebak 29 [11]
UsingWord Vectorsin NLP 18.11.2014 Mark Cieliebak 30 Collobertet al., 2011: •SENNA: GenericNLP System basedon wordvectors •Nomanualfeatureengineering •SolvesmanyNLP-Tasks asgoodasbenchmarksystems [12]
DeepLearning andSentiment Maas et al., 2011 •Enrichwordvectorswithsentimentcontext •Capture semanticofwords(unsupervised) andsentiment(supervised) in parallel, usingmultiple learningtasks wonderful amazing terrible awful 18.11.2014 Mark Cieliebak 31
DeepLearning andSentiment Socher et al. 2013: •Word Vectorsdo not helpforSentiment Analysis •RecursiveNeuralTensor Networks •Representingsentencestructuresastreeswhileaddingsentimentannotationsat same time •Restrictedtosingle, well-structuredsentences • 18.11.2014 Mark Cieliebak 32 [13]
DeepLearning andSentiment QuocandMikolov, 2014: •"Paragraph Vectors" •Add context(sentence, paragraph, document) towordvectorsduringtraining •Improvesmanyexistingapproaches 18.11.2014 Mark Cieliebak 33 [9]
DoesDeepLearning solvethe Sentiment Analysis Problem? 18.11.2014 Mark Cieliebak 34
Conclusion: DeepLearning forSentiment •Small improvements, not revolution •Veryrecentresearch, not yet"end ofthestory" •SemEval2015 will bebenchmark 18.11.2014 Mark Cieliebak 35
Talk in Short! 1.Classic approachesarerule-basedorcorpus-based 2.State-of-the-art toolsclassify4 out of10 docswrong 3.DeepLearning doesnot needhand-craftedfeatures 4.DeepLearning improvesexistingbenchmarks 18.11.2014 Mark Cieliebak 36
ThankYou! Mark Cieliebak ZurichUniversity ofApplied Sciences(ZHAW) Winterthur, Switzerland Email: ciel@zhaw.ch, Website: www.zhaw.ch/~ciel 18.11.2014 Mark Cieliebak 37 [15]

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Can Deep Learning solve the Sentiment Analysis Problem

  • 1. Can Deep Learning solve the Sentiment Analysis Problem? Mark CieliebakZurichUniversity ofApplied Sciences Annual Meeting ofSGAICO –Swiss Group forArtificialIntelligenceandCognitiveScience 18.11.2014
  • 2. Outline 1.What is sentiment analysis? 2.How good are "classical" approaches? 3.Does deep learning solve the problem? 18.11.2014 Mark Cieliebak 2
  • 3. About Me 18.11.2014 Mark Cieliebak 3 Mark Cieliebak Institute of Applied Information Technology (InIT) ZHAW, Winterthur Email: ciel@zhaw.ch, Website: www.zhaw.ch/~ciel Text Analytics Open Data Automated Test Generation Research Interests Software Engineering
  • 4. WhatisSentiment Analysis "… WiFiAnalytics isa freeAndroid appthatI find veryhandywhenitcomestotroubleshootingandmonitoringa homenetwork. "[1] 18.11.2014 Mark Cieliebak 4
  • 5. Sample Application: SocialMedia Monitoring Text AnalyticsComponents: •Find relevant documents •Hot topicAnalysis •Sentiment analysis 18.11.2014 Mark Cieliebak 5 [7]
  • 6. FlavoursofSentiment Analysis •DocumentBased •SentenceBased •Target-Specific •Rating Prediction 18.11.2014 Mark Cieliebak 6
  • 7. Classic ApproachestoSentiment Analysis Rule-Based Corpus-Based 18.11.2014 Mark Cieliebak 7 Predicted Label [3] [4]
  • 8. Simple Sentiment Analysis Idea: Count numberofpositive andnegative words "This cameraisgreat[+1]." +1 (pos) "I find itbeautiful[+1]andgood[+1]." +2 (pos) "Itlooksterrible[-1]." -1 (neg) "This carhasa bluecolor." 0 (neu) POSITIVE: great love nice ... NEUTRAL: hello see I … NEGATIVE: bad hate ugly ... UseSentiment-Dictionary: 18.11.2014 Mark Cieliebak 8
  • 9. Sample Rules 18.11.2014 Mark Cieliebak 9 •DetectBooster Words: "The carisreallyveryexpensive[-1 -1 -2]." •New Category"Mixed": "This carhasan appealing[+1]design andcomfortable[+1]seats, but itisexpensive[-1]." •Negation: Invertonlyscore ofwordsoccuringafter thenegation: "The carisappealing[+3]andI do not[*-1]find itexpensive[-2]" •I do notfind thecarexpensiveanditisappealing. Need to“understand” thesentence
  • 10. Linguistic Analysis -> RULE: Invertscoresofwordsbeingin thesame phrasesasnegation. “I do not find thecarexpensive[+2] anditisappealing[+3].” → +5 (pos) Sentence Sentence Conj. Sentence NounPhrase Verb Phrase Verb Adverb Verb Noun Phrase Adj. Noun Phrase Verb Phrase Det. Det Noun Det. Verb Participle I do not find the car expensive and it is appealing 18.11.2014 Mark Cieliebak 10
  • 11. Rule-BasedSentiment Analysis Most ImportantIssues: -Requiresgoodhand-craftedrules -Hard totransfertonewtasksorlanguages -Doesnot workwellfortextswithbadgrammer(Twitter) 18.11.2014 Mark Cieliebak 11 [5]
  • 12. Classic ApproachestoSentiment Analysis Rule-Based Corpus-Based 18.11.2014 Mark Cieliebak 12 Predicted Label [3] [4]
  • 13. Corpus-BasedSentiment Analysis 18.11.2014 Mark Cieliebak 13 Predicted Label [4]
  • 14. Corpus-BasedSentiment Analysis AnnotatedCorpus Sentence Polarity This analysis is good. Pos It looks awful. Neg This car has a blue color. Neu This car has an appealing design, comfortable seats, but it is expensive. Mix This carhasa veryappealingdesign, comfortableseats, but itisreallyexpensive. Mix This analysis is not good. Neg This car has an appealing design, comfortable seats and it is not expensive. Mix This movie was like a horror event. Neg This carisappealingandisnot expensive. Mix ... ... 18.11.2014 Mark Cieliebak 14
  • 15. Sample Features forTweets •Word ngrams:presence or absence of contiguous sequences of 1, 2, 3, and 4 tokens; noncontiguous ngrams •POS: the number of occurrences of each part-of-speechtag •SentimentLexica: eachwordannotatedwithtonalityscore (-1..0..+1) •Negation: the number of negated contexts •Punctuation: the number of contiguous sequences of exclamation marks, question marks, and both exclamation and question marks •Emoticons: presenceorabsence, last token is a positive or negative emoticon; •Hashtags: the number of hashtags; •Elongatedwords: the number of words with one character repeated (e.g. ‘soooo’) from: Mohammad et al., SemEval2013 18.11.2014 Mark Cieliebak 15
  • 16. Corpus-BasedSentiment Analysis Most ImportantIssues: -Requireslarge annotatedcorpora -Dependson goodfeatures 18.11.2014 Mark Cieliebak 16 [6]
  • 17. HowgoodareSentiment Analysis Tools? 18.11.2014 Mark Cieliebak 17
  • 18. Quick Poll •Short texts: 1-2 sentencesfromTwitter, news, reviewsetc. •Three-classclassification: positive, negative, other •Accuracy= #푐표푟푟푒푐푡푑표푐푠 #푑표푐푠 Mark Cieliebak 21 Accuracy Votes <50% 50-60% 60-70% 70-80% 80-90% >90% "Howgoodarestate-of-the-art sentimentanalysistools?" 18.11.2014
  • 19. Tool Accuracy 0,2 0,3 0,4 0,5 0,6 0,7 0,8 Accuracy Best Tool per Corpus Worst Tool per Corpus 22 61% 40% Avg. 18.11.2014 Mark Cieliebak [14]
  • 20. Tool Accuracy 0,2 0,3 0,4 0,5 0,6 0,7 0,8 Accuracy Best Tool per Corpus Worst Tool per Corpus Overall Best Tool 23 61% 40% 59% Avg. 18.11.2014 Mark Cieliebak
  • 21. Take-Home Lesson Accuracyofbestcommercialtoolon arbitraryshorttextsis59% 18.11.2014 Mark Cieliebak 24
  • 22. ApproachestoSentiment Analysis Rule-Based Corpus-Based 18.11.2014 Mark Cieliebak 25 Predicted Label [9] DeepLearning [8]
  • 23. DeepLearning on Text It'sall aboutWord Vectors! 18.11.2014 Mark Cieliebak 26
  • 24. Word2Vec •Hugesetoftextsamples(billionsofwords) •Extractdictionary •Word-Matrix: k-dimensional vectorforeachword(k typically50-500) •Word vectorinitializedrandomly •Train wordvectorstopredictnextwords, givena sequenceofwordsfromsample text 18.11.2014 Mark Cieliebak 27 Major contributionsbyBengioet al. 2003, Collobert&Weston2008, Socher et al. 2011, Mikolovet al. 2013 [9]
  • 25. The Magic ofWord Vectors 18.11.2014 Mark Cieliebak 28 King -Man + Woman≈ Queen Live Demo on 100b wordsfromGoogle News dataset: http://radimrehurek.com/2014/02/word2vec-tutorial/ [10]
  • 26. Relations LearnedbyWord2Vec 18.11.2014 Mark Cieliebak 29 [11]
  • 27. UsingWord Vectorsin NLP 18.11.2014 Mark Cieliebak 30 Collobertet al., 2011: •SENNA: GenericNLP System basedon wordvectors •Nomanualfeatureengineering •SolvesmanyNLP-Tasks asgoodasbenchmarksystems [12]
  • 28. DeepLearning andSentiment Maas et al., 2011 •Enrichwordvectorswithsentimentcontext •Capture semanticofwords(unsupervised) andsentiment(supervised) in parallel, usingmultiple learningtasks wonderful amazing terrible awful 18.11.2014 Mark Cieliebak 31
  • 29. DeepLearning andSentiment Socher et al. 2013: •Word Vectorsdo not helpforSentiment Analysis •RecursiveNeuralTensor Networks •Representingsentencestructuresastreeswhileaddingsentimentannotationsat same time •Restrictedtosingle, well-structuredsentences • 18.11.2014 Mark Cieliebak 32 [13]
  • 30. DeepLearning andSentiment QuocandMikolov, 2014: •"Paragraph Vectors" •Add context(sentence, paragraph, document) towordvectorsduringtraining •Improvesmanyexistingapproaches 18.11.2014 Mark Cieliebak 33 [9]
  • 31. DoesDeepLearning solvethe Sentiment Analysis Problem? 18.11.2014 Mark Cieliebak 34
  • 32. Conclusion: DeepLearning forSentiment •Small improvements, not revolution •Veryrecentresearch, not yet"end ofthestory" •SemEval2015 will bebenchmark 18.11.2014 Mark Cieliebak 35
  • 33. Talk in Short! 1.Classic approachesarerule-basedorcorpus-based 2.State-of-the-art toolsclassify4 out of10 docswrong 3.DeepLearning doesnot needhand-craftedfeatures 4.DeepLearning improvesexistingbenchmarks 18.11.2014 Mark Cieliebak 36
  • 34. ThankYou! Mark Cieliebak ZurichUniversity ofApplied Sciences(ZHAW) Winterthur, Switzerland Email: ciel@zhaw.ch, Website: www.zhaw.ch/~ciel 18.11.2014 Mark Cieliebak 37 [15]