[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

An Information Retrieval-Based System for Multi-domain Sentiment Analysis

  • Conference paper
  • First Online:
Semantic Web Evaluation Challenges (SemWebEval 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 548))

Included in the following conference series:

Abstract

This paper describes the SHELLFBK system that participated in ESWC 2015 Sentiment Analysis challenge. Our system takes a supervised approach that builds on techniques from information retrieval. The algorithm populates an inverted index with pseudo-documents that encode dependency parse relationships extracted from the sentences in the training set. Each record stored in the index is annotated with the polarity and domain of the sentence it represents; this way, it is possible to have a more fine-grained representation of the learnt sentiment information. When the polarity of a new sentence has to be computed, the new sentence is converted to a query and a two-steps computation is performed: firstly, a domain is assigned to the sentence by comparing the sentence content with domain contextual information learnt during the training phase, and, secondly, once the domain is assigned to the sentence, the polarity is computed and assigned to the new sentence. Preliminary results on an in-vitro test case demonstrated promising results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 35.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://www.cs.jhu.edu/~mdredze/datasets/sentiment/.

  2. 2.

    The package containing instructions for replicating the experiments can be downloaded at http://dkmtools.fbk.eu/moki/demo/SentIRe.zip.

  3. 3.

    http://www.cs.jhu.edu/~mdredze/datasets/sentiment/.

References

  1. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86. Association for Computational Linguistics, Philadelphia, July 2002

    Google Scholar 

  2. Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Aggarwal, C.C., Zhai, C.X. (eds.) Mining Text Data, pp. 415–463. Springer, New York (2012)

    Chapter  Google Scholar 

  3. Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: ACL, pp. 187–205 (2007)

    Google Scholar 

  4. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)

    Article  Google Scholar 

  5. Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: ACL, pp. 271–278 (2004)

    Google Scholar 

  6. Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: WWW, pp. 519–528 (2003)

    Google Scholar 

  7. Paltoglou, G., Thelwall, M.: A study of information retrieval weighting schemes for sentiment analysis. In: ACL, pp. 1386–1395 (2010)

    Google Scholar 

  8. Tan, S., Wang, Y., Cheng, X.: Combining learn-based and lexicon-based techniques for sentiment detection without using labeled examples. In: SIGIR, pp. 743–744 (2008)

    Google Scholar 

  9. Qiu, L., Zhang, W., Hu, C., Zhao, K.: Selc: a self-supervised model for sentiment classification. In: CIKM, pp. 929–936 (2009)

    Google Scholar 

  10. Melville, P., Gryc, W., Lawrence, R.D.: Sentiment analysis of blogs by combining lexical knowledge with text classification. In: KDD, pp. 1275–1284 (2009)

    Google Scholar 

  11. Taboada, M., Brooke, J., Tofiloski, M., Voll, K.D., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)

    Article  Google Scholar 

  12. Turney, P.D.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: ACL, pp. 417–424 (2002)

    Google Scholar 

  13. Somasundaran, S.: Discourse-level relations for Opinion Analysis. Ph.D. thesis, University of Pittsburgh (2010)

    Google Scholar 

  14. Asher, N., Benamara, F., Mathieu, Y.Y.: Distilling opinion in discourse: a preliminary study. In: COLING (Posters), pp. 7–10 (2008)

    Google Scholar 

  15. Wang, H., Zhou, G.: Topic-driven multi-document summarization. In: IALP, pp. 195–198 (2010)

    Google Scholar 

  16. Riloff, E., Patwardhan, S., Wiebe, J.: Feature subsumption for opinion analysis. In: EMNLP, pp. 440–448 (2006)

    Google Scholar 

  17. Wiebe, J., Wilson, T., Bruce, R.F., Bell, M., Martin, M.: Learning subjective language. Comput. Linguist. 30(3), 277–308 (2004)

    Article  Google Scholar 

  18. Wilson, T., Wiebe, J., Hwa, R.: Just how mad are you? finding strong and weak opinion clauses. In: AAAI, pp. 761–769 (2004)

    Google Scholar 

  19. Wilson, T., Wiebe, J., Hwa, R.: Recognizing strong and weak opinion clauses. Comput. Intell. 22(2), 73–99 (2006)

    Article  MathSciNet  Google Scholar 

  20. Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, EMNLP 2003, pp. 129–136. Association for Computational Linguistics, Stroudsburg (2003)

    Google Scholar 

  21. Hatzivassiloglou, V., Wiebe, J.: Effects of adjective orientation and gradability on sentence subjectivity. In: COLING, pp. 299–305 (2000)

    Google Scholar 

  22. Kim, S.M., Hovy, E.H.: Crystal: analyzing predictive opinions on the web. In: EMNLP-CoNLL, pp. 1056–1064 (2007)

    Google Scholar 

  23. Kim, S.M., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automatically assessing review helpfulness. In: EMNLP, pp. 423–430 (2006)

    Google Scholar 

  24. Jakob, N., Gurevych, I.: Extracting opinion targets in a single and cross-domain setting with conditional random fields. In: EMNLP, pp. 1035–1045 (2010)

    Google Scholar 

  25. Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: ICML, pp. 282–289 (2001)

    Google Scholar 

  26. Freitag, D., McCallum, A.: Information extraction with hmm structures learned by stochastic optimization. In: AAAI/IAAI, pp. 584–589 (2000)

    Google Scholar 

  27. Jin, W., Ho, H.H.: A novel lexicalized HMM-based learning framework for web opinion mining. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, pp. 465–472. ACM, New York (2009)

    Google Scholar 

  28. Jin, W., Ho, H.H., Srihari, R.K.: Opinionminer: a novel machine learning system for web opinion mining and extraction. In: KDD, pp. 1195–1204 (2009)

    Google Scholar 

  29. Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: WWW, pp. 342–351 (2005)

    Google Scholar 

  30. Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase dependency parsing for opinion mining. In: EMNLP, pp. 1533–1541 (2009)

    Google Scholar 

  31. Su, Q., Xu, X., Guo, H., Guo, Z., Wu, X., Zhang, X., Swen, B., Su, Z.: Hidden sentiment association in chinese web opinion mining. In: WWW, pp. 959–968 (2008)

    Google Scholar 

  32. Qiu, G., Liu, B., Bu, J., Chen, C.: Expanding domain sentiment lexicon through double propagation. In: IJCAI, pp. 1199–1204 (2009)

    Google Scholar 

  33. Qiu, G., Liu, B., Bu, J., Chen, C.: Opinion word expansion and target extraction through double propagation. Comput. Linguist. 37(1), 9–27 (2011)

    Article  Google Scholar 

  34. Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: COLING (Posters), pp. 36–44 (2010)

    Google Scholar 

  35. Bermingham, A., Smeaton, A.F.: Classifying sentiment in microblogs: is brevity an advantage? In: CIKM, pp. 1833–1836 (2010)

    Google Scholar 

  36. Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Standford University (2009)

    Google Scholar 

  37. Cambria, E., Hussain, A.: Sentic Computing: Techniques, Tools, and Applications. SpringerBriefs in Cognitive Computation. Springer, Dordrecht (2012)

    Book  Google Scholar 

  38. Cambria, E., Hussain, A.: Sentic album: content-, concept-, and context-based online personal photo management system. Cognitive Comput. 4(4), 477–496 (2012)

    Article  Google Scholar 

  39. Wang, Q.F., Cambria, E., Liu, C.L., Hussain, A.: Common sense knowledge for handwritten chinese recognition. Cognitive Comput. 5(2), 234–242 (2013)

    Article  Google Scholar 

  40. Yang, H., Callan, J., Si, L.: Knowledge transfer and opinion detection in the TREC 2006 blog track. In: TREC (2006)

    Google Scholar 

  41. Pan, S.J., Ni, X., Sun, J.T., Yang, Q., Chen, Z.: Cross-domain sentiment classification via spectral feature alignment. In: WWW, pp. 751–760 (2010)

    Google Scholar 

  42. Bollegala, D., Weir, D.J., Carroll, J.A.: Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Trans. Knowl. Data Eng. 25(8), 1719–1731 (2013)

    Article  Google Scholar 

  43. Xia, R., Zong, C., Hu, X., Cambria, E.: Feature ensemble plus sample selection: domain adaptation for sentiment classification. IEEE Int. Syst. 28(3), 10–18 (2013)

    Article  Google Scholar 

  44. Yoshida, Y., Hirao, T., Iwata, T., Nagata, M., Matsumoto, Y.: Transfer learning for multiple-domain sentiment analysis–identifying domain dependent/independent word polarity. In: AAAI, pp. 1286–1291 (2011)

    Google Scholar 

  45. Ponomareva, N., Thelwall, M.: Semi-supervised vs. cross-domain graphs for sentiment analysis. In: RANLP, pp. 571–578 (2013)

    Google Scholar 

  46. Tsai, A.C.R., Wu, C.E., Tsai, R.T.H., Hsu, J.Y.: Building a concept-level sentiment dictionary based on commonsense knowledge. IEEE Int. Syst. 28(2), 22–30 (2013)

    Article  Google Scholar 

  47. Tai, Y.J., Kao, H.Y.: Automatic domain-specific sentiment lexicon generation with label propagation. In: iiWAS, pp. 53:53–53:62. ACM (2013)

    Google Scholar 

  48. Huang, S., Niu, Z., Shi, C.: Automatic construction of domain-specific sentiment lexicon based on constrained label propagation. Knowl. Based Syst. 56, 191–200 (2014)

    Article  Google Scholar 

  49. Dragoni, M.: Shellfbk: an information retrieval-based system for multi-domain sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation, SemEval ’2015, pp. 502–509. Association for Computational Linguistics, Denver, June 2015

    Google Scholar 

  50. da Costa Pereira, C., Dragoni, M., Pasi, G.: Multidimensional relevance: prioritized aggregation in a personalized information retrieval setting. Inf. Process. Manage. 48(2), 340–357 (2012)

    Article  Google Scholar 

  51. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60. Association for Computational Linguistics, Baltimore, June 2014

    Google Scholar 

  52. van Rijsbergen, C.J.: Information Retrieval. Butterworth, London (1979)

    MATH  Google Scholar 

  53. Dragoni, M., Tettamanzi, A.G., da Costa Pereira, C.: Propagating and aggregating fuzzy polarities for concept-level sentiment analysis. Cognitive Comput. 7(2), 186–197 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. FBK–IRST, Trento, Italy

    Giulio Petrucci & Mauro Dragoni

  2. University of Trento, Trento, Italy

    Giulio Petrucci

Authors
  1. Giulio Petrucci
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mauro Dragoni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mauro Dragoni .

Editor information

Editors and Affiliations

  1. Inria, Sophia Antipolis, France

    Fabien Gandon

  2. INRIA Sophia-Antipolis Méditerranée, Sophia Antipolis, France

    Elena Cabrio

  3. Université Paris-Sorbonne, Paris, France

    Milan Stankovic

  4. École des Mines de Saint-Étienne, Saint-Étienne, France

    Antoine Zimmermann

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Petrucci, G., Dragoni, M. (2015). An Information Retrieval-Based System for Multi-domain Sentiment Analysis. In: Gandon, F., Cabrio, E., Stankovic, M., Zimmermann, A. (eds) Semantic Web Evaluation Challenges. SemWebEval 2015. Communications in Computer and Information Science, vol 548. Springer, Cham. https://doi.org/10.1007/978-3-319-25518-7_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25518-7_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25517-0

  • Online ISBN: 978-3-319-25518-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation