Looking for Opinion in Land-Use Planning Corpora

  • Eric Kergosien
  • Cédric Lopez
  • Mathieu Roche
  • Maguelonne Teisseire
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8404)

Abstract

A great deal of research on opinion mining and sentiment analysis has been done in specific contexts such as movie reviews, commercial evaluations, campaign speeches, etc. In this paper, we raise the issue of how appropriate these methods are for documents related to land-use planning. After highlighting limitations of existing proposals and discussing issues related to textual data, we present the method called Opiland (OPinion mIning from LAND-use planning documents) designed to semi-automatically mine opinions in specialized contexts. Experiments are conducted on a land-use planning dataset, and on three datasets related to others areas highlighting the relevance of our proposal.

Keywords

Land-use planning Text-Mining Opinion-mining Corpus Lexicon 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bestgen, Y.: Building affective lexicons from specific corpora for automatic sentiment analysis. In: Proceedings of LREC, Trento, Italy, pp. 496–500 (2008)Google Scholar
  2. 2.
    Buléon, P., Méo, G.D.: L’espace social. Armand Colin, Annales de la recherche urbaine (2005)Google Scholar
  3. 3.
    Cambria, E., Havasi, C., Hussain, A.: Senticnet 2: A semantic and affective resource for opinion mining and sentiment analysis. In: FLAIRS Conference. AAAI Press (2012)Google Scholar
  4. 4.
    Debarbieux, B., Vanier, M.: Ces territorialités qui se dessinent. Editions de l’Aube, 267 pages, Datar (2002)Google Scholar
  5. 5.
    Duthil, B., Trousset, F., Roche, M., Dray, G., Plantié, M., Montmain, J., Poncelet, P.: Towards an automatic characterization of criteria. In: Hameurlain, A., Liddle, S.W., Schewe, K.-D., Zhou, X. (eds.) DEXA 2011, Part I. LNCS, vol. 6860, pp. 457–465. Springer, Heidelberg (2011)Google Scholar
  6. 6.
    Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: 5th Conference on Language Resources and Evaluation, pp. 417–422 (2006)Google Scholar
  7. 7.
    Fan, W., Sun, S., Song, G.: Sentiment classification for chinese netnews comments based on multiple classifiers integration. In: Proc. of the Int. Joint Conf. on Comp. Sciences and Optimization, pp. 829–834 (2011)Google Scholar
  8. 8.
    Joshi, A., Balamurali, P., Bhattacharyya, P., Mohanty, R.: C-feel-it: a sentiment analyzer for microblogs. In: Proc. of HLT, pp. 127–132 (2011)Google Scholar
  9. 9.
    Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence 22(2), 110–125 (2006)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Klebanov, B., Beigman, E., Diermeier, D.: Vocabulary choice as an indicator of perspective. In: Proceedings of the ACL 2010, Conference Short Papers, ACLShort 2010, pp. 253–257. Association for Computational Linguistics, Stroudsburg (2010)Google Scholar
  11. 11.
    Lafourcade, M.: Making people play for lexical acquisition. In: Proc. 7th Symposium on Natural Language Processing (SNLP 2007), pp. 13–15 (2007)Google Scholar
  12. 12.
    Liu, B.: Sentiment analysis and opinion mining, p. 167. Morgan and Claypool Publishers (2012)Google Scholar
  13. 13.
    Pak, A., Paroubek, P.: Microblogging for micro sentiment analysis and opinion mining. TAL 51(3), 75–100 (2010)Google Scholar
  14. 14.
    Piolat, A., Booth, R., Chung, C., Davids, M., Pennebaker, J.: La version française du dictionnaire pour le liwc: modalités de construction et exemples d’utilisation. Psychologie Française 56(3), 145–159 (2011)CrossRefGoogle Scholar
  15. 15.
    Plutchik, R.: The nature of emotions. American Scientist 89(4), 344–350 (2001)CrossRefGoogle Scholar
  16. 16.
    Rice, D.R., Zorn, C.: Corpus-based dictionaries for sentiment analysis of specialized vocabularies. In: Proceedings of NDATAD 2013: New Directions in Analyzing Text as Data Workshop 2013, London, England (2013)Google Scholar
  17. 17.
    Plantié, M., Roche, M., Dray, G., Poncelet, P.: Is a voting approach accurate for opinion mining? In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2008. LNCS, vol. 5182, pp. 413–422. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Torres-Moreno, J., El-Beze, M., Bechet, F., Camelin, N.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proc. of ACL, pp. 417–424 (2009)Google Scholar
  19. 19.
    Turney, P.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proc. of ACL, pp. 417–424 (2002)Google Scholar
  20. 20.
    Vanier, M.: Territoires, territorialité, territorialisation - controverses et perspectives. In: PUR, pp. 417–424 (2002)Google Scholar
  21. 21.
    Wiebe, J., Riloff, E.: Finding mutual benefit between subjectivity analysis and information extraction. IEEE Transactions on Affective Computing 2(4), 175–191 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Eric Kergosien
    • 1
    • 2
  • Cédric Lopez
    • 4
  • Mathieu Roche
    • 3
  • Maguelonne Teisseire
    • 1
    • 2
  1. 1.LIRMM - CNRSUniv. Montpellier 2Montpellier Cedex 5France
  2. 2.Irstea, UMR TETISMontpellier Cedex 5France
  3. 3.Cirad, UMR TETISMontpellier Cedex 5France
  4. 4.Viseo - Objet DirectGrenobleFrance

Personalised recommendations