SUPERVISORS: PASCAL PONCELET, MATHIEU ROCHE

Transcription

1 CONTEXT, SCIENTIFIC, SOCIAL AND ECONOMIC ISSUES Various analytic firms offer advanced conversation and sentiment analysis tools within the corporate services marketplace. While the true effectiveness of social media analytics is debated, their pricing and marketing ensures that they are exclusive tools in limited supply. The result is what could be described as an arms race in the area of conversation analysis, wherein already powerful stakeholders (brand marketers, public relations firms, corporate lobbies, political parities etc.) are far better equipped to leverage the social, political and economic gains attributed to Web 2.0 than less-well resourced groups and individual Internet users. This hardly seems to be a recipe for reinvigorating democracy. Our project represents, in part, an effort to even out the analytics arms race and provide more people with better tools to make sense of the multitude of information and conversations that take place online. Moreover, companies are interested in the results of our project, in order to improve decision support tools for business intelligence. POSITION OF THE PHD PROJECT Sentiment analysis is a hot topic for Natural Language Processing (NLP) and data mining. Major conferences in these fields propose specific sessions about this crucial topic to evaluate people s pulse. But currently, the methods are often based on basic analysis with statistics concerning the positive and negative words in documents. More sophisticated methods are proposed, but, in general, they are not integrated in a global system which takes into account several areas (e.g. NLP, data mining, communication). Moreover, these methods are often adapted to one language and/or specific kinds of texts (movies reviews, product reviews, blogs, tweets, and so forth). In our project, we propose a generic method to develop a method for fine-grained analysis of texts. A more subtle analysis will be possible by the use of new media communication skills and the acquisition of a more precise sentiment model. The precision of the model that we propose is given by (1) a complete emotion classification, (2) a sentiment analysis relative to a topic or a sub-topic from a text. This work will allow us to analyze modern communication media (tweets, blogs, etc.) which use a very specific and evolving vocabulary. Our automatic methods will be based on seed knowledge, enriched incrementally. So, the development of our approaches will be independent of the domain and of the language (French, Spanish, English, etc.). We will focus our work on Environment domain. Actually, this topic represents a priority for the research in Montpellier (Pôle de Compétitivité) and for the NUMEV Labex (leader: LIRMM) and GEOSUD Equipex (leader: TETIS). For instance NUMEV seeks to harmonize the approaches of hard sciences and life and environmental sciences in order to pave the way for an emerging interdisciplinary group with an international profile. STATE-OF-THE-ART In natural language processing, subjectivity analysis and the study of the ways people express their opinions in texts (blogs, customer reviews, letters to editors, etc.) is known as sentiment analysis or opinion mining. A comprehensive survey of this work can be found in (Pang and Lee 2008) and in special issues of journals (Roche and Poncelet 2009; El-Bèze et al. 2010). Subjectivity detection aims at separating opinions statements from facts (Wiebe et al. 1999; Kim and Hovy, 2004). Polarity recognition attempts to classify texts according to the Sentiment analysis and social network 1/6

2 positivity or negativity of the opinions expressed in them. There are two main approaches: one based on counting how many positive and how many negative terms appear in each text (Turney 2002), and one based on machine learning from annotated training data (Pang et al, 2002; Esuli and Sebastiani, 2006). Hybrid approaches were shown to be the best (Kennedy and Inkpen 1996; Wiegand and Klakow, 2010). Other tasks are emotion classification (Neviarouskaya et al. 2010), product comparison (Liu et al 2005), and opinion summarization (Carenini et al. 2006). In our project, we will focus our text-mining approaches for sentiment analysis on blog or micro blogs, in particular in health and environment domain. Currently, Twitter represents the most popular medium for micro-blogs. Emerging topics on Twitter are studied with application of anomaly detection techniques; for example, the Sequentially Discounting Normalized Maximum Likelihood (Takahashi, Tomioka et al. 2011) study uses relationships based on tweet metadata, omitting the text content analysis. In the context of blog and micro blog studies, specific features can strengthen sentiment mining, such as emoticons (Alec et al 2009, Davidov et al. 2010, Ruan 2011), hashtags (Barbosa and Feng 2010), and so forth. The normalisation of these features by using dictionaries, specific heuristics (Joshi et al. 2011), and speech recognition devices can be useful (Kobus et al. 2008; Beaufort et al. 2010). These different features are exploited by machine learning methods which use pre-existing opinion corpora (training data). An annotated training corpus according to sentiment classes is crucial for supervised machine learning. A lot of corpora are available in Text-Mining Challenges such as TREC (Text REtrieval Conference), but only a few are annotated for opinions and polarities, for product reviews. A corpus of tweets annotated with polarities can be automatically obtained using specific features such as emoticons, by selecting tweets containing only positive (resp. negative) emoticons (Alec et al 2009). This corpus and the associated features were used in order to build a prediction model of sentiment (Rafrafi et al 2011). The model depends of the domain (i.e., the corpus used to learn the model). Moreover, several classification methods can be gathered in voting systems proposed by LIRMM (Plantié et al. 2008) or by applying boosting and/or bagging methods (Fan et al. 2011). It is an open question to what extent these results extend to the data stream context in which we will be working. DESCRIPTION OF THE WORK The work is divided in three main parts: (1) Topic feature extraction, (2) sentiment feature extraction, (3) topic and sentiment combination. Each task can be studied independently, although there are links between topic and sentiment features extraction from Web 2.0 data. Actually, these features can have some lexical similarities, such as character elongation (e.g. words with repeated letters, such as cooooooooooooool). Note that the proposed methods will be generic and independent of languages. (1) Topic feature extraction The aim of this first task is to extract lexical knowledge about a given topic. We plan to start with different semantic resources: general ones (e.g., WordNet) or specialized ones (e.g., Agrovoc, Mesh). This enables us to have different terms related to a given topic. Moreover, we plan to use external resources such as GeoNames and specific gazetteers in order to extract geospatial information in documents (Loglisci et al. 2012). This information is important in order to associate a location with a sentiment, for improving the analysis of environment management. Sentiment analysis and social network 2/6

3 We can enrich these terms by using text-mining techniques (e.g. Latent Dirichlet Allocation) applied to the real data. In addition we can exploit the thematic information of the data (e.g., hashtags in tweets) in order predict a topic (Davidov et al. 2010). But the main difficulty of this task is to detect the lexical information in the Web 2.0 documents, because the features can be really specific. Actually, the real data have a lot of noise (e.g. deleted, added, or swapped characters), and use specific vocabulary (Pak and Paroubek 2010). The initial step here should be elimination of spam from the corpora we will work with. It is well known, for instance, that any corpus of twitter messages will contain a sizeable subset of spam messages: advertising messages, messages cut at 140 characters, non-text messages, etc. Such spam messages, making data noisy, can be retrieved in the corpus and labelled manually as spam as long as the corpus is not very large. A solution to the problem of the size of the corpus will be the use of semi-supervised classification, where it is sufficient to label only a small-size training set, and provide a large unlabeled training set. To discover these new features in real Web 2.0 documents (for instance, cinéma is similar to 6néma ) we must combine lexical measures (e.g. Levenshtein distance) with different heuristics by considering phonetic aspects. We have to propose new heuristics for two main reasons: (1) Scability: With a large amount of data, it is impossible to compare all the words; we have to reduce the search space by taking into account only a part of textual data. (2) Short word processing: In general, lexical measures are inefficient for calculating the similarity between the short words that are very frequent in the Web 2.0 data. So we have to propose (1) new approaches and (2) new measures in order to tackle these two issues. The original combination of existing methods (e.g. semi-supervised classification of textual data) and new approaches will allow us to produce a rich lexical resource regarding the studied topic. (2) Sentiment feature extraction The limits of the SentiWordNet Knowledge As noted earlier, in the sentiment analysis domain, SentiWordNet (Esuli and Sebastiani, 2006) is the oft-used resource. Each feature of this resource is associated to three numerical scores describing the term intensity according the objectivity, positivity, and negativity criteria. The method used to develop SentiWordNet is based on the quantitative analysis of the glosses associated to sets of synonyms (synsets), and on the use of the resulting vector term representations for semi-supervised synset classification (Esuli and Sebastiani, 2006). This important resource has two main problems. First, it is not really adapted to the new communication model (new words, phrases, and so forth). Moreover, the three criteria proposed are not enough for predicting a precise sentiment. In this context, we propose different methods to enrich the sentiment model of SentiWordNet. Our sentiment representation described in the following section will take into account specific abbreviation (e.g., lol) and emoticons (:-)). This type of information can show a precise emotion, such as happiness, sadness, anger, or sarcasm (Davidov et al. 2010; Ruan 2011). A more adapted sentiment classification of features The information regarding the polarity and the intensity of sentiments is often insufficient. Actually, the different types of emotions are not distinguished in this model. In our project, we will detail the types of sentiment by adapting the Hourglass model (Plutchik 2001) which was recently used in (Cambria et al ; Cambria et al. 2012). The sentiment model of Hourglass is based on four independent dimensions representing the emotional state of the Sentiment analysis and social network 3/6

4 mind (i.e., Sensitivity, Aptitude, Attention, Pleasantness). Each of the four affective dimensions is characterized by six levels which determine the intensity of the expressed/perceived emotion. Note that this model enables the different affective sentiments to co-exist as compound emotions (e.g., love and aggressiveness). We plan to represent the sentiment features of the texts by using this kind of model that is more precise than the SentiWordNet knowledge. Other types of sentiment can be added and/or combined with the emotion model we want to exploit in our project (Ekman 1992; Mohammad and Turney 2010). The seed sentiment information Data from the Web 2.0 have lexical specificities. For instance, some words may be shortened or lengthened. So, the phenomena of elongation of letters (e.g. the word cooooooool) can detect a kind of sentiment (Joshi et al. 2011, Brody and Diakopoulos 2011). In addition, emoticons (i.e., set of characters representing an emotion) in the data can also reveal a sentiment that can be automatically detected (Davidov et al. 2010). In the model of our project we will able to integrate this lexical information such as smiley classification according the emotion (Ruan 2011). W plan to study the lexical specificities of these linguistic features together with New Media Communication Specialists about Web 2.0. Finally, we will determine if all these characteristics depend of geographical and/or temporal information (Eisenstein et al. 2010). We can also study if they depend of a specific community (Chikhi et al. 2009). This lexical study can be useful in order to enrich the base of sentiment knowledge. This enables our methods to automatically learn rules to detect: (1) semantic relationships between features (e.g. cooooooool is synonym to cool) (2) modification of the intensity of a sentiment (e.g. cooooooool has a greater intensity than cool) Note that the words are not only sufficient for the lexical acquisition. In fact the n-grams of words are crucial in sentiment analysis (Vernier et al 2007; Pak and Paroubek 2010). They will be taken into account in our project. All these rules and studies will represent the seed information of our emotion model. (3) Topic and Sentiment Combination Another important challenge is about the combination of topics and sentiments. The sentiment-carrying words can behave differently according to the given topic. Let consider the two following sentences "The picture quality of this camera is high" and "The ceilings of the building are high". In the first one (i.e., an expressed opinion on a movie), the adjective high is considered as positive. In the second sentence (i.e., a document about architecture), this adjective is neutral. This example shows that an adjective is very correlated with a specific domain. To address this challenge, we plan to combine topic and sentiment features. Two ways will be studied. First, we can extract topics (or sub-topics such as picture quality for the camera topic) and sentiments independently. Then we have to combine these two concepts. Second, we plan to propose an original algorithm which extracts topics (or sub-topics) and sentiments simultaneously. This type of algorithm will be based on the use of association rules and/or sequential patterns developed at LIRMM in Montpellier. Moreover it will be necessary to identify the issuer and/or the target of the sentiment. For example, an actor may be issuer of a sentiment but also produce it. For this difficult treatment, we will perform fine contextual analysis based on NLP approaches (combination of lexical, syntactic, and semantic analysis). Sentiment analysis and social network 4/6

5 DESCRIPTION OF THE LABORATORIES Laboratory of Informatics, Robotics, and Microelectronics of Montpellier (LIRMM) - LIRMM ( is a 350-person cross-faculty research entity of the University Montpellier 2 and the National Center for Scientific Research (CNRS). LIRMM research activities cover a broad range of topics, including: modeling of complex systems, research on algorithms, bioinformatics, human-machine interaction, robotics, database, distributed systems, AI, knowledge engineering and more. LIRMM s Informatics department counts 85 permanent researchers, and more than 70 PhD candidates. Several research groups have good expertise in text-mining and data mining that could be relevant for this joint project. Moreover the LIRMM researches of this project participate at the NUMEV Labex 1. LIRMM obtained A+ during the AERES 2010 evaluation. In addition LIRMM has chosen health and environment as priority domains of application for its research in Computer Science. Territoires, Environnement, Télédétection et Information Spatiale (TETIS) - The joint research unit (UMR) Geoinformation and Earth Observation for Environment and Land Management (UMR TETIS, Irstea, CIRAD, AgroParisTech) focuses on developing methods for acquiring and deploying geoinformation to enhance environmental and territorial knowledge and management. An integrated approach is thus implemented throughout the information flow channel, from the acquisition of data (mobilization) until the use of knowledge (appropriation), with the processing, production, management and pooling of geoinformation involved along the way. The UMR TETIS research team conducts conceptual, methodological and thematic research to deal with the different components of this information flow channel, with four main lines of research: remote sensing, geoinformation, acquisition and processing, analysis of spatial structures and spatiotemporal dynamics, Information system design, Information and territorial development. Approaches are developed in remote sensing, computer science, spatial analysis, geography, environmental science and territorial development. Moreover, the TETIS lab leads the national project GEOSUD Equipex 2. REFERENCES Alec G., H. Lei, and B. Richa (2009). Twitter sentiment classification using distant supervision. Computer and Information Science, Vol. 150, Issue: 12, 1-6. Barbosa L. and Feng J. (2010). Robust Sentiment Detection on Twitter from Biased and Noisy Data. In Proceedings of COLING. pp Brody S. and N. Diakopoulos (2011). Using Word Lengthening to Detect Sentiment in Microblogs. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp Cambria E., R. Speer, and C. Havasi (2010). SenticNet: a Publicly Available Semantic Resource for Opinion Mining. In Proceedings of the AAAI Symposium on Common Sense Knowledge. Cambria E., C. Havasi, and A. Hussain (2012). SenticNet 2: A semantic and affective resource for opinion mining and sentiment analysis. In: FLAIRS, pp , Marco Island Carenini G, R.T. Ng, and A. Pauls (2006). Multi-document summarization of evaluative text. In Proceedings of EACL, pp Chikhi N.F., B. Rothenburger, and N. Aussenac-Gilles (2009). Community Structure Identification: A Probabilistic Approach. Proceedings of ICMLA, pp Davidov D., O. Tsur, and A. Rappoport (2010). Enhanced sentiment learning using twitter hashtags and smileys. In Proceedings of International Conference on Computational Linguistics (COLING) Sentiment analysis and social network 5/6

6 Ekman P. (1992). An Argument for Basic Emotions, Cognition and Emotion, 6, El-Bèze M, A. Jackiewicz, S. Hunston, Traitement Automatique des Langues 2010 Volume 51 Num. 3 Eisenstein J., B. O'Connor, N.A. Smith, E.P. Xing (2010). A Latent Variable Model for Geographic Lexical Variation. In Proceedings of EMNLP, pp Esuli A. and F. Sebastiani (2006) SentiWordNet: A Publicly Available Lexical Resource for Opinion Mining. In 5th Conference on Language Resources and Evaluation, pp Fan W., S. Sun, G. Song (2011). Sentiment Classification for Chinese Netnews Comments Based on Multiple Classifiers Integration. Proceedings of the Int. Joint Conf. on Comp. Sciences and Optimization, pp Joshi A A. Balamurali P. Bhattacharyya P., and Mohanty R. (2011). C-feel-it: a sentiment analyzer for microblogs. Proccedings of HLT 2011 Kennedy A. and D. Inkpen (2006). Sentiment Classification of Movie Reviews Using Contextual Valence Shifters. Computational Intelligence 22(2): Kim S-M and E. Hovy (2004). Determining the sentiment of opinions. In Proceedings of COLING, pp Kobus C., F. Yvon, and G. Damnati (2008). Normalizing SMS: are two metaphors better than one? In 22nd International Conference on Computational Linguistics, Loglisci C., D. Ienco, M. Roche, M. Teisseire, D. Malerba (2012) An Unsupervised Framework for Topological Relations Extraction from Geographic Documents. In proceedings of DEXA (2), LNCS, Springer-Verlag Liu B., M. Hu, and J. Cheng (2005). Opinion observer: Analyzing and comparing opinions on the web. In Proceedings of the International Conference on World Wide Web (WWW), pp Mohammad S.M., and P.D. Turney (2010). Emotions evoked by common words and phrases: Using Mechanical Turk to create an emotion lexicon, Proceedings of the NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, LA, California, Muthukrishnan S. (2005). Data Streams: Algorithms and Applications. Now Publishers Inc. Neviarouskaya A., H. Prendinger, and M. Ishizuka (2011). Affect Analysis Model: Novel Rule-based Approach to Affect Sensing from Text. Int. Journal of Natural Language Engineering, 17(1): Pak A and P. Paroubek (2010). Microblogging for Micro Sentiment Analysis and Opinion Mining. TAL 51(3): Pang B. and L. Lee (2008). Opinion mining and sentiment analysis. Found. and Trends in IR., 2(1-2), pp Pang B., L. Lee, and S. Vaithyanathan (2002) Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of EMNLP, pp Plantié M., M. Roche, G. Dray, and P. Poncelet (2008). Is a Voting Approach Accurate for Opinion Mining? In proceedings of DaWaK 08 (Int. Conf. on Datawarehousing and Knwoledge Discovery), p Plutchik R (2001). The nature of emotions. American Scientist, 89(4): Rafrafi A. V. Guigue, and P. Gallinari (2011). Pénalisation des mots fréquents pour la classification de sentiments. Les Cahiers du numérique, Vol. 7. pp Roche M., P. Poncelet [Ed.] (2009). Fouille de Données d'opinions. Special issue of RNTI (Revue des Nouvelles Technologies de l'information), 202 pages, volume E-17. Ruan L (2011). Meaningful Signs - Emoticons. Theory and Practice in Language Studies, 1(1), Takahashi, T., R. Tomioka, et al. (2011). Discovering Emerging Topics in Social Streams via Link Anomaly Detection IEEE International Conference on Data Mining (ICDM) Turney P. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of ACL, pp Vernier M., Mathet Y., Rioult François, Charnois T., Ferrari S., and Legallois D. (2007). Classification de textes d opinions : une approche mixte n-grammes et sémantique. In Proceedings of DEFT Wiebe J. and E. Riloff (2011). Finding Mutual Benefit between Subjectivity Analysis and Information Extraction, IEEE Transactions on Affective Computing, 2 (4): Wiegand M. and D. Klakow (2010) Bootstrapping supervised machine-learning polarity classifiers with rule-based classification. Proc. of the Work. on Comp. App. to Subjectivity and Sentiment Analysis, ECAI, pp Sentiment analysis and social network 6/6