A. Ananiadou, J. Chruszcz, J. Keane, J. Mcnaught, and P. Watry, The national centre for text mining: aims and objectives

]. I. Ariadneag06, E. Antonellis, and . Gallopoulos, Exploring term-document matrices from matrix models in text mining, SIAM Text Mining Workshop, 6th SIAM SDM, 2005.

S. [. Agrawal, R. Rajagopalan, Y. Srikant, and . Xu, Mining newsgroups using networks arising from social behavior, Proceedings of the twelfth international conference on World Wide Web , WWW '03, 2003.
DOI : 10.1145/775152.775227

S. [. Andreevskaia and . Bergler, Mining wordnet for fuzzy sentiment: sentiment tag extraction from wordnet glosses, EACL, 2006.

A. [. Adomavicius and . Tuzhilin, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering, vol.17, issue.6, p.734749, 2005.
DOI : 10.1109/TKDE.2005.99

T. [. Ando and . Zhang, A high-performance semi-supervised learning method for text chunking, Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics , ACL '05, p.19, 2005.
DOI : 10.3115/1219840.1219841

P. [. Bloehdorn, A. Cimiano, ]. Hothobh01, G. Budanitsky, and . Hirst, Learning ontologies to improve text clustering and classication Semantic distance in wordnet: an experimental, application-oriented evaluation of ve measures, 29th Annual Conference of the German Classication Society Workshop on WordNet and Other Lexical Resources, 2nd Meeting of the North American Chapter of the ACL, p.334341, 2001.

A. [. Bloehdorn and . Hotho, Text classication by boosting weak learners based on terms and concepts, 4th ICDM, pp.331-334, 2004.

A. [. Blei, M. Ng, and . Jordan, Latent dirichlet allocation, Journal of Machine Learning Research, vol.3, p.9931022, 2003.

]. D. Bou92 and . Bourigault, Surface grammatical analysis for the extraction of terminological noun phrases, 14th COLING-92, pp.977-981, 1992.

L. [. Brin and . Page, The anatomy of a large-scale hypertextual Web search engine, Computer Networks and ISDN Systems, vol.30, issue.1-7, pp.1-7107117, 1998.
DOI : 10.1016/S0169-7552(98)00110-X

W. [. Blake and . Pratt, Better rules, fewer features: a semantic approach to selecting features from text, Proceedings 2001 IEEE International Conference on Data Mining, p.5966, 2001.
DOI : 10.1109/ICDM.2001.989501

Y. [. Balabanovic and . Shoham, Fab: content-based, collaborative recommendation, Communications of the ACM, vol.40, issue.3, pp.66-72, 1997.
DOI : 10.1145/245108.245124

L. [. Cohen and . Hunter, Natural language processing and systems biology. Articial Intelligence methods and tools for systems biology, 2004.

A. [. Cimiano, S. Hotho, and . Staab, Learning concept hierarchies from text corpora using formal concept analysis, Journal of Articial Intelligence Research, vol.24, p.305339, 2005.

W. [. Cong, H. Lee, B. Wu, and . Liu, Semi-supervised text classication using partitioned em, 9th DASFAA, pp.482-493, 2004.

S. [. Caropreso, F. Matwin, G. Sebastiani, R. T. Carenini, E. Ng et al., A learnerindependent evaluation of the usefulness of statistical phrases for automated text categorization. Text Databases and Document Management: Theory and Practice Extracting knowledge from evaluative text, pp.78102-1118, 2001.

[. Carrington, J. Scott, and S. Wasserman, Models and Methods in Social Network Analysis, 2005.
DOI : 10.1017/CBO9780511811395

E. [. Dascalu, S. Chioasca, and . Trausan-matu, ASAP- An Advanced System for Assessing Chat Participants, Articial Intelligence: Methodology, Systems, and Applications, 2008.
DOI : 10.1007/978-3-540-85776-1_6

E. [. Daille, J. Gaussier, and . Langé, Towards automatic extraction of monolingual and bilingual terminology, Proceedings of the 15th conference on Computational linguistics -, pp.515-521, 1994.
DOI : 10.3115/991886.991975

B. [. Ding and . Liu, The utility of linguistic rules in opinion mining, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '07, 2007.
DOI : 10.1145/1277741.1277921

]. C. Elk07 and . Elkan, Method and system for selecting documents by measuring document quality. US Patent 7200606, 2007.

]. A. Es05a, F. Esuli, and . Sebastiani, Determining the semantic orientation of terms through gloss classication, CIKM-05, p.617624, 2005.

]. A. Es05b, F. Esuli, and . Sebastiani, Sentiwordnet: A publicly available lexical resource for opinion mining, LREC, 2005.

M. [. Eirinaki and . Vazirgiannis, Usage-Based PageRank for Web Personalization, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005.
DOI : 10.1109/ICDM.2005.148

]. J. Fir57 and . Firth, A synopsis of linguistic theory 1930-1955. Studies in Linguistic Analysis, p.132, 1957.

T. [. Furnkranz, E. Mitchell, and . Rilo, A case study in using linguistic phrases for text categorization on the www, Working Notes of the AAAI / ICML, Workshop on Learning for Text Categorization, p.512, 1998.

]. D. Fre98 and . Freitag, Machine Learning for Information Extraction in Informal Domains, 1998.

M. [. Fisher, H. T. Smith, and . Welser, You Are Who You Talk To: Detecting Roles in Usenet Newsgroups, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06), 2006.
DOI : 10.1109/HICSS.2006.536

L. [. Fan, S. Wallace, Z. Rich, and . Zhang, Tapping the power of text mining, Communications of the ACM, vol.49, issue.9, p.7682, 2006.
DOI : 10.1145/1151030.1151032

P. [. Ghose, A. Ipeirotis, and . Sundararajan, Opinion mining using econometrics: A case study on reputation systems, ACL, 2007.

D. [. Goldberg, B. M. Nichols, D. Oki, and . Terry, Using collaborative ltering to weave an information tapestry, Communications of the ACM, vol.35, issue.12, p.6170, 1992.

A. Harb, G. Dray, M. Plantié, P. Poncelet, M. Roche et al., Détection d'opinion: apprenons ls bons adjectifs! In Atelier FOuille des Données d'OPinions (FODOP 08, en conjonction avec le 26ème Congrès Informatique des Organisations et Systèmes d'Information et de Décision (INFORSID 08), p.5966, 2008.

]. M. Hea94 and . Hearst, Multi-paragraph segmentation of expository text, 32nd ACL, p.916, 1994.

]. M. Hea99 and . Hearst, Untangling text data mining, Proc. of the 37th ACL, p.310, 1999.

J. [. Herlocker, L. G. Konstan, and J. T. Terveen, Evaluating collaborative ltering recommender systems, ACM TOIS, 2004.
DOI : 10.1145/963770.963772

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

B. [. Hu and . Liu, Mining and summarizing customer reviews, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.168-177, 2004.
DOI : 10.1145/1014052.1014073

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

R. [. Helander, Y. Lawrence, and . Liu, Looking for great ideas, Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis , WebKDD/SNA-KDD '07, 2007.
DOI : 10.1145/1348549.1348557

]. M. Hls-+-07, E. Hu, A. Lim, H. W. Sun, B. Lauw et al., Measuring article quality in wikipedia: Models and evaluation, CIKM '07, 2007.

K. [. Hatzivassiloglou and . Mckeown, Predicting the semantic orientation of adjectives, Proceedings of the 8th conference on European chapter of the Association for Computational Linguistics, p.174181, 1997.

]. A. Hos05 and . Hoskinson, Creating the ultimate research assistant, IEEE Computer, vol.38, issue.11, p.9799, 2005.

. Hpt-+-02-]-l, J. C. Hirschman, J. Park, L. Tsujii, C. Wong et al., Accomplishments and challenges in literature data mining for biology, Bioinformatics, issue.12, p.1815531561, 2002.

L. [. Jindal and . Bing, Identifying comparative sentences in text documents, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '06, p.244251, 2006.
DOI : 10.1145/1148170.1148215

J. [. Jalam, J. Chauchat, and . Dumais, Automatic recognition of keywords using n-grams, 16th Symposium of IASC (COMPSTAT 04), p.12451254, 2004.

X. [. Java, T. Song, B. Finin, and . Tseng, Why we twitter, Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis , WebKDD/SNA-KDD '07, p.5666, 2007.
DOI : 10.1145/1348549.1348556

B. [. Kohrs and . Merialdo, Improving collaborative ltering for new-users by smart object selection, Oral presentation, 2001.

M. [. Kamps, R. J. Marx, M. Mokken, and . De-rijke, Using wordnet to measure semantic orientations of adjectives, 4th LREC, p.11151118, 2004.

]. H. Koz93 and . Kozima, Text segmentation based on similarity between words, 31st ACL, 1993.

S. [. Kao and . Poteet, Report on kdd conference 2004 panel discussion -can natural language processing help text mining? SIGKDD Explorations, p.132133, 2004.

S. [. Kao and . Poteet, Text mining and natural language processing -introduction for the special issue, SIGKDD Explorations, vol.7, issue.1, p.12, 2006.

V. [. Kehagias, V. G. Petridis, P. Kaburlasos, and . Fragkou, A comparison of word-and sense-based text categorization using several classication algorithms, Journal of Intelligent Information Systems, vol.21, issue.3, p.227247, 2001.

]. K. Kri75 and . Krippendor, Information Theory. Structural Models for Qualitative Data, 1975.

B. [. Kageura and . Umino, Methods of automatic term recognition, Technology Journal, vol.3, issue.2, p.259289, 1996.

]. D. Lew92 and . Lewis, An evaluation of phrasal and clustered representations on a text categorization task, SIGIR, p.3750, 1992.

]. D. Lin98 and . Lin, Automatic retrieval and clustering of similar words, COLING-ACL, 1998.

]. B. Liu07 and . Liu, Web Data Mining -Exploring Hyperlinks, Contents and Usage Data, 2007.

R. [. Mooney and . Bunescu, Mining knowledge from text using information extraction, SIGKDD Explorations, 2006.
DOI : 10.1145/1089815.1089817

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

]. D. Mbc-+-05, Y. Metzler, W. B. Bernstein, A. Croft, J. Moat et al., Similarity measures for tracking information ow, CIKM, p.517524, 2005.

[. Mitra, C. Buckley, A. Singhal, and C. Cardie, An analysis of statistical and syntactic phrases, 5th International

W. [. Miller and . Charles, Contextual correlates of semantic similarity, Conference Recherche d' Information Assistee par Ordinateur, p.128, 1991.
DOI : 10.1016/S0022-5371(64)80011-6

]. A. Mcc05 and . Mccallum, Information extraction: Distilling structured data from unstructured text, ACM Queue, vol.3, issue.9, 2005.

P. [. Maurel, L. Curtoni, and . Dini, L'analyse des sentiments dans les forums, Atelier FOuille des Données d'OPinions (FODOP 08), en conjonction avec le 26ème Congrès Informatique des Organisations et Systèmes d'Information et de Décision, 2008.

M. [. Mladenic and . Grobelnik, Word sequences as features in text-learning, 7th Electrotechnical and Computer Science Conference, p.145148, 1998.

]. B. Mob07 and . Mobasher, Data mining for personalization Adaptive Web: Methods and Strategies of Web Personalization, LNCS, vol.4321, pp.90-135, 2007.

H. [. Manning and . Schutze, Foundations of Statistical Natural Language Processing, 1999.

S. [. Nenadic and . Ananiadou, Mining semantically related terms from biomedical literature, ACM Transactions on Asian Language Information Processing, vol.5, issue.1, p.2243, 2006.
DOI : 10.1145/1131348.1131351

R. [. Nigam and . Ghani, Analyzing the eectiveness and applicability of co-training, 8th CIKM, p.8693, 2000.

D. [. Pazzani and . Billsus, Learning and revising user proles: The identication of interesting web sites, Machine Learning, vol.27, p.313331, 1997.

E. [. Pennock, S. Horvitz, C. L. Lawrence, and . Giles, Collaborative ltering by personality diagnosis: A hybrid memory-and model-based approach, Sixteenth Conference on Uncertainty in Articial Intelligence, p.473480, 2000.

L. [. Pang, S. Lee, and . Vaithyanathan, Thumbs up? sentiment classication using machine learning techniques, Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP), p.7986, 2002.

M. [. Plantié, G. Roche, P. Dray, and . Poncelet, Is a Voting Approach Accurate for Opinion Mining?, 10th International Conference on Datawarehousing and Knowledge Discovery, 2008.
DOI : 10.1007/978-3-540-85836-2_39

. M. Rac-+-02-]-a, I. Rashid, D. Albert, S. K. Cosley, S. M. Lam et al., Getting to know you: Learning new user preferences in recommender systems, IUI, 2002.

R. [. Rajman and . Besançon, Stochastic distributional models for textual information retrieval, 9th ASMDA, p.8085, 1999.

]. P. Res95 and . Resnik, Using information content to evaluate semantic similarity in a taxonomy, 14th IJCAI, p.448453, 1995.

]. P. Res99 and . Resnik, Semantic similarity in a taxonomy: an informationbased measure and its application to problems of ambiguity in natural language, Journal of Articial Intelligence Research, vol.11, p.95130, 1999.

]. E. Ric79 and . Rich, User modeling via stereotypes, Cognitive Science, vol.3, p.329354, 1979.

]. E. Ril95 and . Rilo, Little words can make a big dierence for text classication, 18th SIGIR, p.130136, 1995.

N. P. Resnick, M. Iacovou, P. Suchak, and J. Bergstrom, GroupLens, Proceedings of the 1994 ACM conference on Computer supported cooperative work , CSCW '94, p.175186, 1994.
DOI : 10.1145/192844.192905

]. S. Rob77 and . Robertson, The probability ranking principle in ir, Journal of Documentation, vol.33, p.294304, 1977.

]. G. Sal88 and . Salton, Syntactic approaches to automatic book indexing, 26th ACL, p.120138, 1988.

S. [. Spasic, J. Ananiadou, A. Mcnaught, and . Kumar, Text mining and ontologies in biomedicine: Making sense of raw text, Briefings in Bioinformatics, vol.6, issue.3, p.239251, 2005.
DOI : 10.1093/bib/6.3.239

P. [. Stavrianou, N. Andritsos, and . Nicoloyannis, Overview and semantic issues of text mining, ACM SIGMOD Record, vol.36, issue.3, p.2334, 2007.
DOI : 10.1145/1324185.1324190

]. E. Sap21 and . Sapir, Language: an introduction to the study of speech, HAR- COURT BRACE and CO, 1921.

J. [. Stavrianou and . Chauchat, Opinion mining issues and agreement identication in forum texts, Atelier FOuille des Données d'OPinions (FODOP 08), en conjonction avec le 26ème Congrès Informatique des Organisations et Systèmes d'Information et de Décision, p.5158, 2008.

]. R. Sch99 and . Schapire, A brief introduction to boosting, 16th IJCAI, p.14011405, 1999.

]. F. Seb02 and . Sebastiani, Machine learning in automated text categorization, ACM Computing Surveys, vol.34, issue.1, p.147, 2002.

]. F. Seb06 and . Sebastiani, Classication of text, automatic. The Encyclopedia of Language and Linguistics, p.457462, 2006.

]. C. Sha48 and . Shannon, A mathematical theory of communication, Bell System Technical Journal, vol.27, p.379423, 1948.

M. [. Salton and . Lesk, Computer Evaluation of Indexing and Text Processing, Journal of the ACM, vol.15, issue.1, p.836, 1968.
DOI : 10.1145/321439.321441

P. [. Shardanand and . Maes, Social information ltering: Algorithms for automating word of mouth, CHI-95, 1995.

N. [. Swanson and . Smalheiser, Assessing a gap in the biomedical literature: magnesium deciency and neurologic disease, Neuroscience Research Communications, vol.15, issue.1, p.19, 1994.

N. [. Swanson and . Smalheiser, An interactive system for nding complementary literatures: a stimulus to scientic discovery, Articial Intelligence, vol.91, p.183203, 1997.

P. [. Scripps, A. Tan, and . Esfahanian, Node roles and community structure in networks, Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis , WebKDD/SNA-KDD '07, p.2635, 2007.
DOI : 10.1145/1348549.1348553

J. [. Stavrianou, J. Velcin, and . Chauchat, Denition and measures of an opinion model for mining forums, 2009 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 09), 2009.

T. [. Seco, J. Veale, and . Hayes, An intrinsic information content metric for semantic similarity in wordnet, 16th ECAI, p.10891090, 2004.

A. [. Salton, C. S. Wong, and . Yang, A vector space model for automatic indexing, Communications of the ACM, vol.18, issue.11, pp.613-620, 1975.
DOI : 10.1145/361219.361220

M. [. Turney and . Littman, Measuring praise and criticism, ACM Transactions on Information Systems, vol.21, issue.4, p.315346, 2003.
DOI : 10.1145/944012.944013

T. [. Trausan-matu and . Rebedea, Polyphonic Inter-Animation of Voices in VMT. Studying Virtual Math Teams, Computer- Supported Collaborative Learning Series 11, 2009.

]. P. Tur02 and . Turney, Thumbs up or down? semantic orientation applied to unsupervised classication of reviews, ACL-2002, p.417424, 2002.

J. [. Velcin and . Ganascia, Topic Extraction with AGAPE, Proc. of the International Conference on Advanced Data Mining and Applications, 2007.
DOI : 10.1007/978-3-540-73871-8_35

URL : https://hal.archives-ouvertes.fr/hal-01336130

. Vvr-+-05-]-g, E. Varelas, P. Voutsakis, E. Raftopoulou, E. E. Petrakis et al., Semantic similarity methods in wordnet and their application to information retrieval on the web, 7th WIDM, p.1016, 2005.

Z. [. Witten, M. Bray, B. Mahoui, and . Teahan, Text mining: a new frontier for lossless compression, Proceedings DCC'99 Data Compression Conference (Cat. No. PR00096), 1999.
DOI : 10.1109/DCC.1999.755669

]. J. Wie00 and . Wiebe, Learning subjective adjectives from corpora, 2000.

]. D. Yar95 and . Yarowsky, Unsupervised word sense disambiguation rivaling supervised methods, 33rd ACL, p.189196, 1995.

L. [. Yeh, A. A. Hirschman, and . Morgan, Evaluation of text data mining for database curation: lessons learned from the KDD Challenge Cup, Bioinformatics, vol.19, issue.Suppl 1, pp.331-339, 2003.
DOI : 10.1093/bioinformatics/btg1046

J. [. Yang and . Pedersen, A comparative study on feature selection in text categorization, 14th ICML, p.412420, 1997.

M. [. Zhang, L. Ackerman, and . Adamic, Expertise networks in online communities, Proceedings of the 16th international conference on World Wide Web , WWW '07, p.221230, 2007.
DOI : 10.1145/1242572.1242603

]. O. Zai98 and . Zaiane, From resource discovery to knowledge discovery on the internet, 1998.

J. [. Zhang, T. Callan, and . Minka, Novelty and redundancy detection in adaptive ltering, SIGIR-02, 2002.

. .. Du-retour, il fout la pression, il sait qu'il aura peanuts comme rendu de boulot en plus , mais sur celui qui bosse en silence , là où il y a des chances d'avoir Pour faire la mesure, ces collégues là , non seulement ils t'emmerdent quand tu bosses, n' ont aucun scrupule à laisser le ollectif assurer pour eux le boulot qu'ils ne font pas mais en plus ils se permettent de venir baver sur le boulot des autres.... C'est les mêmes qui sont persuadés qu'occuper un poste est synonyme de travailler et qu'en plus il faudrait leur derouler un tapis rouge pour accepter d'être là le matin , tellement ils sont plus geniaux que les autres. cultiver en France plus que toute autre : Il y a un juste milieu à tout, ça sut du système binaire du tout ou rien, c frustrant à la n, il faudrait pouvoir temporiser. Cheers 303 & Ah les team building sessions! ca me fait toujours rire, ce qui signie qu'ils n'exprimeront jamais ce qu'ils pensent reellement

C. 'est-considere-comme-impoli, Mais ils savent eux qu'ils n'ont pas de potes au boulot, uniquement des collegues avec qui ils sont en competition permanente, vu qu'on est sans cesse evaluee par nos superieurs, et qu'a la n de l'annee le bonheur des uns (augmentations , primes, etc..) fait le malheur des autres (0% d'augmentation, retrogadation, avertissement, licenciement) Cela n'empeche pas de travailler ensemble en bon ordre et sans trop de probleme, car chacun accepte ce systeme