Classic method : An overview of the main process: 1- Preprocessings and Vector space modelisation
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1 Gestion dynamique des connaissances, «la vanne de l information» Actionneur de la boucle de contrôle Inertie psychologique Controverse Acceptabilité := Risque admis Risque Perception S informer Production de Connaissances Scores partiels Représentation Définir une stratégie Évaluation Multicritère Liste ordonnée des solutions retenues Sélection Argumenter Sélection des Connaissances discriminantes Rhétorique de la Logique décisionnelle Estimation du risque pour un classement et une stratégie donnés Dimensions les plus pertinentes pour les acquisitions ultérieures d information Signal de contrôle Non* consensus drisque dt Risque à retenir la solution la plus stratégique Révision Évaluer le risque Sensibilité de la stratégie Distances entre les solutions éligibles, l ignorance et l idéal Système interactif d aide à la décision (Recommandation) Boucle de contrôle (Automatisation cognitive) 1/50 Classic method : An overview of the main process: 1- Preprocessings and Vector space modelisation Complete Index Index reduction Reduced Index Text Vectors Text Vectors Text Vectors Reduced Vectors Reduced Vectors Reduced Vectors Learning Corpus (Test and learning sets) Is a voting approach accurate for opinion mining? 2
2 Classic method : An overview of the main process: 2- Modelisation and Classification (Training Corpus) Reduced Vectors Reduced Vectors Reduced Vectors Reduced Vectors Classification Model (Test Corpus) Reduced Vectors Reduced Vectors Reduced Vectors Reduced Vectors Assigned Class for each Vector Is a voting approach accurate for opinion mining? 3 Extraction automatisée de CA pour l évaluation multicritère Classic method : 2 étapes de classification Deux phases principales : Extraction de jugements de valeur et attribution à un critère d évaluation Affectation d un score au jugement de valeur Extraction des CAs Cartographie Evaluation d intention des CAs Attribution d un score 4/50 5 Extraction automatisée de CAs
3 Web opinion mining: How to extract opinions from blogs? Ali Harb, Michel Plantié, Gérard Dray, Mathieu Roche, François Trousset, Pascal Poncelet (LGI2P/EMA LIRMM) Nîmes France 5 Outline Introduction State of the art «AMOD» method Results on movie domain Test on another domain Conclusion and future work 6
4 Introduction Opinion detection on the Web New techniques to express opinions are more and more easy to use! We always have an opinion on anything!! Analyse expressed opinions: What about my public image? I want to buy a new camera! It is raining... What about viewing Indiana Jones movie? 7 Introduction Blogs phenomenon importance millions of blogs blogs created every day 35% of net surfers rely on opinions posted on blogs. 44% of net surfers have stopped a purchase when seeing a negative opinion on a blog 91% think that the web has a great or medium importance in making up its own opinion regarding a company image. Sources : Médiamétrie, EIAA, Forrester, Technorati (août 2007), OpinionWay
5 Introduction: One example of blog 9 Aggregation tools for opinions and journals 10
6 Classification vs Opinion Classification Classification Classify documents according to their theme: sport, cinema, literature, Word Comparisons (bag of words approach) Goal, Football, Transfer, Blues => SPORT Class Opinion Classification Classify documents according to their general feeling (positive vs. negative) More difficult than traditional classification approaches: how to catch a particular opinion? 11 State of the art Unsupervised opinion classification Turney Algorithm (2002) Input: opinion documents Output : classified documents (positive vs. negative) 1. Morphosyntaxic analysis to identify sentences 2. Semantic Orientation (SO) estimation of extracted sentences 3. Assignment of a document to a class (positive vs. negative) 12
7 State of the art Class assignment Average computation of SOs for a document > 0 : positive < 0 : negative Problems : Negative opinion expressions are very often softer than positive ones Adverbs may invert polarity 13 State of the art: Difficulties Do we use the same adjectives in different domains? The chair is comfortable The movie is comfortable???? Same adjectives may have different meaning in different domains or contexts The picture quality of this camera is high (positive) The ceilings of the building are high (neutral) 14
8 Outline Introduction State of the art Automatic Mining of Opinion Dictionnaries (AMOD) method Results on movie domain Test on another domain Conclusion and future work 15 Input: PMots = {good, nice, excellent, positive, fortunate, correct, superior}, NMots = {bad, nasty, poor, negative, unfortunate, wrong, inferior}, one domain Output: New adjectives specific to one domain 1. Ask a search engine 2. Search for significant adjectives 3. Eliminate «noisy adjecives» 4. Run another time this algorithm to find new significant adjectives 16
9 AMOD: Ask a search engine Example of request with google and the word good "+opinion +review +cinema +good bad -nasty - poor -negative -unfortunate -wrong -inferior" 17 AMOD: Ask a search engine Results 7 * * docs 300 docs nice good bad poor Positive words 4200 documents Negative words 18
10 AMOD: Search for significant adjectives Association rule usage Item : adjective Transaction : sentence time window WS1 The movie is amazing, good acting, a lots of great action and the popcorn was delicious WS2 19 AMOD: Eliminate «noisy» adjectives Rule Example Positive excellent, good funny nice, good great nice encouraging good different Negative Bad, wrong boring Bad, wrong commercial poor current bad different Common adjective suppression 20
11 AMOD: Eliminate «noisy» adjectives How to eliminate useless adjectives? with hits Mutual Information PMI(w1,w2)=log2(p(w1&w2)/p(w1)*p(w2)) Cubic Mutual Information Favor frequent co-occurrences IM3(w1,w2)= log2(nb(w1&w2)^3/nb(w1)*nb(w2)) AcroDefIM3 IM3 + Domain information log2(hit((w1&w2) and C)^3/hit(w1 and C)*hit(w2 and C)) 21 AMOD: Eliminate «noisy» adjectives Use of AcroDefIM3 measure to get rid of noisy adjectives Positives Negatives excellent, good : funny (20,49) bad, wrong : boring (8,33) nice, good : great (12,50) bad, wrong : commercial (3,054) nice : encouraging (0,001) poor : current (0,0002) 22
12 State of the art Class assignment The movie is bad (negative) The movie is not bad (rather positive) The movie is not bad, there is a lot of 6 1 funny moments 23 AMOD: Class assignment Use of averbs inverting polarity 1. The movie isn t good 2. The movie isn t amazing at all 3. The movie isn t very good 4. The movie isn t too good 5. The movie isn t so good 6. The movie isn t good enough 7. The movie is neither amazing nor funny 1, 2, 7 : inversion 3, 4, 5 : + 30% 6 : -30% 24
13 Outline Introduction State of the art «AMOD» method Results on movie domain Test on another domain Conclusion and future work 25 Experiments on Movie domain Learning phase: blogsearch.google.fr Test : Movie Review Data (positive and negative reviews of Internet Movie Database) 2 data sets very differents (blogs vs journalists) Positives PL NL Seeds L. 66,9% 7 7 Negatives PL NL Seeds L. 30,49%
14 Classification with learned adjectives WS-S Positives LP LN 1-1% 67,2% WS-S Negatives LP LN 1-1% 39,2% WS-S: Window Size support value Best results with WS=1 and support=1% 27 Learned adjectives, AcroDef, reinforcement Learned Adjectives and AcrodefIM3 WS-S Positives PL NL 1-1% 75,9% WS-S Negatives LP LN 1-1% 46,7% Reinforcement (a learned word become a seed word) WS-S Positives PL NL 1-1% 82,6% WS-S Negatives PL NL 1-1% 52,4%
15 Influence of the learning set size Relation between corpus size and number of learned adjectives Nmber of learned adjectives Size of the learning set for each seed word From 250 documents 29 Comparison with a classic method Precision=Ratio of pertinent documents found in regard to all documents (pertinent or not) found Recall = Number of pertinent documents found in regard to all document of the knowledge base or corpus Fscore = Precision * Recall / (Precision+Recall) Classic Positives Negatives FSCORE 60,5% 60,9% AMOD Positives Negatives FSCORE 71,73% 62,2% 30
16 Test on another domain Learning on automobile domain (car) Tests : 40 documents from WS S Positive LP LN 1 1% 57,5% WS S Positif LP LN Learned Adj 1 1% 87,5% AcroDef 1 1% 92,5% Reinf 1 1% 95% Conclusion and future work AMOD approach is very encouraging To extract positive and negative adjectives for opinion mining tasks Domain specific adjectives Experiments show very good results to classify opinion texts Method is independant of the domain Automatically build opinion documents training corpora Future work: Enhance the classification procedure Use this tool to built training corpora and apply other classifications algorithms Extract other kind of words Extend to other classification tasks such as criteria 32 classification
17 THANK YOU.. 33 References R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In VLDB 94,1994. A. Andreevskaia and S. Bergler. Semantic tag extraction from wordnet glosses [3] K. Church and P. Hanks. Word association norms, mutual information, and lexicography. In Computational Linguistics, volume 16, pages 22 29, D. Downey, M. Broadhead, and O. Etzioni. Locating complex named entities in web text. In Proceedings of IJCAI 07, pages , M. Hu and B. Liu. Mining and summarizing customer reviews. In Proceedings of KDD 04, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, J. Kamps, M. Marx, R. J. Mokken, and M. Rijke. Using wordnet to measure semantic orientation of adjectives. In Proceedings of LREC 2004, the 4th International Conference on Language Resources and Evaluation, pages , Lisbon, Portugal, G. Miller. Wordnet: A lexical database for english. In Communications of the ACM, M. Plantié, M. Roche, G. Dray, and P. Poncelet. Is a voting approach accurate for opinion mining? In Proceedings of the 10th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 08 ), Torino Italy, V. Risbergen. Information retrieval, 2nd edition. In Butterworths, London, M. Roche and V. Prince. AcroDef: A Quality Measure for Discriminating Expansions of Ambiguous Acronyms. In Proceedings of CONTEXT, Springer-Verlag, LNCS, pages ,
18 Classification with learned adjectives WS-S Positives LP LN 1-1% 67,2% % 60,3% % 65,6% % 57,6% % 56,8% % 68,4% % 28,9% % 59,3% % 67,3% WS-S Negatives LP LN 1-1% 39,2% Sentences identification morpho-syntactic analysis on documents TreeTagger «On ne change pas une équipe qui gagne» On PRO:PER on ne ADV ne change VER:PRES changer pas ADV pas une DET:ART un équipe NOM équipe qui PRO:REL qui gagne VER:PRES gagner. SENT. 36
19 How to learn opinions in a specific domain? AMOD Method Input: PMots = {good, nice, excellent, positive, fortunate, correct, superior}, NMots = {bad, nasty, poor, negative, unfortunate, wrong, inferior}, one domain Output: New adjectives specific to one domain 1. Ask a search engine 2. Search for significant adjectives 3. Eliminate «noisy adjecives» 4. Run another time this algorithm to find new significant adjectives 37 Semantic orientation estimation (1/3) Use of PMI-IR (Pointwise Mutual Information and Information Retrieval) PMI between 2 words, w1 and w2 PMI(w1,w2)=log2(p(w1&w2)/p(w1)*p(w2)) p(w1&w2) : probability that w1 and w2 appear together PMI : > 0 words tend to appear together < 0 words do not tend to appear together 38
20 Semantic orientation estimation (2/3) Semantic orientation (SO) of a word SO-PMI(word) = _ pword PWords PMI(word,pword) _ nword NWords PMI (word,nword) PWords = {good, nice, excellent, positive, fortunate, correct, superior} NWords = {bad, nasty, poor, negative, unfortunate, wrong, inferior} 39 Semantic orientation estimation (3/3) PMI-IR : PMI evaluation by executing requests on search engines and counting the number of hits _ pwords PWords hits(word NEAR pword) * _ nwords NWords hits(nword) SO-PMI(word) = _ pwords PWords hits(pword) * _ nmots NMots hits(word NEAR nword) ο With search engine (altavista : operator NEAR, Google : «m1 * m2») 40
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