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Reseach Article

Approaches, Tools and Applications for Sentiment Analysis Implementation

by Alessia D'Andrea, Fernando Ferri, Patrizia Grifoni, Tiziana Guzzo
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 125 - Number 3
Year of Publication: 2015
Authors: Alessia D'Andrea, Fernando Ferri, Patrizia Grifoni, Tiziana Guzzo

Alessia D'Andrea, Fernando Ferri, Patrizia Grifoni, Tiziana Guzzo . Approaches, Tools and Applications for Sentiment Analysis Implementation. International Journal of Computer Applications. 125, 3 ( September 2015), 26-33. DOI=10.5120/ijca2015905866

@article{ 10.5120/ijca2015905866,
author = { Alessia D'Andrea, Fernando Ferri, Patrizia Grifoni, Tiziana Guzzo },
title = { Approaches, Tools and Applications for Sentiment Analysis Implementation },
journal = { International Journal of Computer Applications },
issue_date = { September 2015 },
volume = { 125 },
number = { 3 },
month = { September },
year = { 2015 },
issn = { 0975-8887 },
pages = { 26-33 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2015905866 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:15:04.214745+05:30
%A Alessia D'Andrea
%A Fernando Ferri
%A Patrizia Grifoni
%A Tiziana Guzzo
%T Approaches, Tools and Applications for Sentiment Analysis Implementation
%J International Journal of Computer Applications
%@ 0975-8887
%V 125
%N 3
%P 26-33
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

The paper gives an overview of the different sentiment classification approaches and tools used for sentiment analysis. Starting from this overview the paper provides a classification of (i) approaches with respect to features/techniques and advantages/limitations and (ii) tools with respect to the different techniques used for sentiment analysis. Different application fields of application of sentiment analysis such as: business, politic, public actions and finance are also discussed in the paper.

  1. Liu, B. 2006. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer.
  2. Bethard, S., Hong, Y., Thornton, A., Hatzivassiloglou, V., Jurafsky, D. 2004. Automatic extraction of opinion propositions and their holders. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text.
  3. Wiebe, J. & Riloff, E. 2005. Creating subjective and objective sentence classifiers from unannotated texts. Computational Linguistics and Intelligent Text Processing, 2005, pp. 486-497.
  4. Nasukawa ,T. & Yi, J. 2003. Sentiment analysis: capturing favorability using natural language processing. In Proceedings of the 2nd international conference on Knowledge capture, October 23–25, 2003. (pp. 70–77). Florida, USA.
  5. Morinaga, S., Yamanishi, K., Tateishi, K., Fukushima, T. 2002. Mining product reputations on the web. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 341-349.
  6. Pang, B., Lee, L., Vaithyanathan, S. 2002. Thumbs up? Sentiment Classification using Machine Learning Techniques. Proc. of 7th EMNLP, pp.79-86.
  7. Tong, R.M. 2001. An operational system for detecting and tracking opinions in on-line discussion. In Proceedings of SIGIR Workshop on Operational Text Classification.
  8. Turney, P. 2002. Thumbs up or thumbs down? semantic orientation applied to unsupervised classifcation of reviews. In Proceedings of the 40th ACL, pp. 417-424.
  9. Wiebe, J. (2000) Learning subjective adjectives from corpora. In Proceedings of National Conference on Artificial Intelligence.
  10. Wilson, T., Wiebe, J., Hoffmann, P. 2009. Recognizing contextual polarity: An exploration of features forphrase-level sentiment analysis. Computational Linguistics, 35(3), pp. 399-433.
  11. Hatzivassiloglou, V. & McKeown, K.R. 1997. Predicting the semantic orientation of adjectives. In Proceedings of the 8th conference on European chapter of the association for computationnal linguistics Madrid, Spain, pp.174-181.
  12. Pang, B., & Lee, L. 2004. A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd annual meeting of the Association for Computational Linguistics (ACL), pp. 271–278. Barcelona, Spain
  13. Yi, J., Nasukawa, T., Niblack, W., Bunescu, R. 2003. Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. In Proceedings of the 3rd IEEE international conference on data mining (ICDM 2003), November 19–22, 2003, pp. 427-434 Florida, USA.
  14. Hiroshi, K., Tetsuya, N., Hideo, W. 2004. Deeper sentiment analysis using machine translation technology. In Proceedings of the 20th international conference on computational linguistics (COLING 2004), August 23-27, pp. 494-500, Geneva, Switzerland.
  15. Bollen, J., Mao, H., Zeng, X. 2011. Twitter mood predicts the stock market. Journal of Computational Science, 2(1), pp. 1-8.
  16. Smith, A.N., Fischer, E., Yongjian, C. 2012. How does brand-related user-generated content differ across across YouTube, Facebook, and Twitter? Journal of Interactive Marketing, 26(2), pp. 102-113.
  17. Abbasi, A., Fu, T., Zeng, D., Adjeroh, D. 2013. Crawling Credible Online Medical Sentiments for Social Intelligence. Proceedings of the ASE/IEEE International Conference on Social Computing.
  18. Fu, T., Abbasi, A., Zeng, D., Chen, H. 2012. Sentimental Spidering: Leveraging Opinion Information in Focused Crawlers. ACM Transactions on Information Systems, 30(4), 24.
  19. Abbasi, A., Chen, H., Salem, A. 2008. Sentiment Analysis in Multiple Languages: Feature Selection for Opinion Classification in Web Forums. ACM Transactions on Information Systems, 26(3), 12.
  20. Pang, B., & Lee, L. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, Vol 2 (1-2).
  21. Niu, Y., Zhu, X., Li, J., Hirst, G. 2005. Analysis of polarity information in medical text. In Proceedings of the American Medical Informatics Association 2005 Annual Symposium.
  22. Balahur, A., Kozareva, Z., Montoyo, A. 2009. Determining the polarity and source of opinions expressed in political debates.
  23. Ku, L.W., Li, L.Y., Wu, T.H., Chen, H.H. 2005. Major topic detection and its application to opinion summarization. In Proceedings of the ACM Special Interest Group on Information Retrieval (SIGIR), Salvador, Brasil.
  24. Bansal, M., Cardie, C., Lee, L. 2008. The power of negative thinking: Exploiting label disagreement in the min-cut classification framework. In Proceedings of the International Conference on Computational Linguistics (COLING), 2008. Poster paper, pp.15-18.
  25. Terveen, L., Hill, W., Amento, B., McDonald, D., Creter, J. 1997. PHOAKS: A system for sharing recommendations. Communications of the Association for Computing Machinery (CACM), 40(3), PP. 59-62
  26. Kim, S.M. & Hovy, E. 2006. Automatic identification of pro and con reasons in online reviews. In Proceedings of the COLING/ACL Main Conference Poster Sessions, pp. 483-490.
  27. Medhat, W., Hassan, A., Korashy, H. 2014. Sentiment analysis algorithms and applications: A survey, Ain Shams Eng.
  28. Mullen, T., & Collier, N. 2004. Sentiment Analysis using Support Vector Machines with Diverse Information Sources. Proc. of 9th EMNLP, pp. 412-418.
  29. Kudo, T., & Matsumoto, Y. 2004. A Boosting Algorithm for Classification of Semi-Structured Text. In EMNLP, Vol. 4, pp. 301-308.
  30. Jebaseeli, A. N., & Kirubakaran, E. 2012. A survey on sentiment analysis of (product) reviews. International Journal of Computer Applications, 47(11).
  31. Kaur, A., & Gupta, V. 2013. A survey on sentiment analysis and opinion mining techniques. Journal of Emerging Technologies in Web Intelligence, 5(4), 367-371.
  32. Maynard, D., & Funk, A. 2011. Automatic detection of political opinions in tweets. In: Proceedings of the 8th international conference on the semantic web, ESWC’11, p. 88-99.
  33. Hu, X., Tang, J., Gao. H., Liu, H. 2013. Unsupervised sentiment analysis with emotional signals. In International Conference on World Wide Web.
  34. Syed, A.Z., Aslam, M., Martinez-Enriquez, A.M. 2014. Associating targets with SentiUnits: a step forward in sentiment analysis of Urdu text. Artificial Intelligence Review, 41(4), pp. 535-561.
  35. Abdulla, N. A., Ahmed, N. A., Shehab, M. A., Al-Ayyoub, M., Al-Kabi, M. N., & Al-rifai, S. 2014. Towards improving the lexicon-based approach for arabic sentiment analysis. International Journal of Information Technology and Web Engineering (IJITWE), 9(3), 55-71.
  36. Read, J. (2005) Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In ACL Student Research Workshop, pp 43-48
  37. Tausczik, Y.R. & Pennebaker, J.W. 2010. The psychological meaning of words: Liwc and computerized text analysis methods. Journal of Language and Social Psychology, 29(1):24-54
  38. Dodds, P.S. & Danforth, C.M. 2009. Measuring the happiness of large-scale written expression: songs, blogs, and presidents. Journal of Happiness Studies, 11(4):441-456
  39. Bradley, M.M., Lang, P.J. 1999. Affective norms for English words (ANEW): Stimuli, instruction manual and affective ratings. Technical Report C-1, Gainesville, FL: The Center for Research in Psychophysiology, University of Florida
  40. Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A. 2010. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), pp. 2544-2558
  41. Bermingham, A., & Smeaton, A.F. 2010. Classifying Sentiment in Microblogs: Is Brevity an Advantage? In ACM International Conference on Information and Knowledge Management (CIKM), pp. 1833-1836
  42. Paltoglou, G., & Thelwall, M. 2012. Twitter, MySpace, Digg: Unsupervised Sentiment Analysis in Social Media. ACM Transactions on Intelligent Systems and Technology (TIST), 3(4):66:1–66:19
  43. Esuli, A., & Sebastiani, F. 2006. Sentiwordnet: A publicly available lexical resource for opinion mining. In Proceedings of LREC Vol. 6, pp. 417-422
  44. Miller, G.A. 1995. WordNet: a lexical database for English. Communications of the ACM, 38(11), 39-41
  45. Gonçalves, P., Benevenuto, F., Cha, M. 2013. Panas-t: A pychometric scale for measuring sentiments on twitter. arXiv preprint arXiv:1308.1857
  46. Watson, D. & Clark, L. 1985. Development and validation of brief measures of positive and negative affect: the panas scales. Journal of Personality and Social Psychology, 54(1):1063–1070
  47. Wang, C.J., Tsai, M.F., Liu, T., Chang, C.T. 2013. Financial Sentiment Analysis for Risk Prediction. In Proceedings of the Sixth International Joint Conference on Natural Language Processing pp. 802-808
  48. Cambria, E., Speer, R., Havasi, C., Hussain, A. 2010. SenticNet: A Publicly Available Semantic Resource for Opinion Mining. In AAAI Fall Symposium: Commonsense Knowledge Vol. 10, p. 2 Lecture Notes in Computer Science, 5449,CICLing 2009:468-480
  49. Mayfield, E., & Rosé, C.P. 2012. LightSIDE: Open Source Machine Learning for Text Accessible to Non-Experts. In the Handbook of Automated Essay Grading, Routledge Academic Press
  50. Hassan, A., Abbasi, A., Zeng, D. 2013. Twitter Sentiment Analysis: A Bootstrap Ensemble Framework, Proceedings of the ASE/IEEE International Conference on Social Computing, pp. 357-364
  51. Abbasi , A. 2010. Intelligent Feature Selection for Opinion Classification. IEEE Intelligent Systems, 25(4), pp. 75-79.
  52. Fan, T.K., & Chang, C.H. 2011. Blogger-centric contextual advertising. Expert Systems with Applications, 38(3), pp. 1777-1788
  53. Qiu, G., He, X., Zhang, F., Shi, Y., Bu, J., Chen, C. 2010. DASA: Dissatisfaction-oriented Advertising based on Sentiment analysis. Expert Systems with Applications, 37 pp.6182-6191
  54. Kang, H., Yoo, S.J., Han, D. 2012. Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews. Expert Systems with Applications ,39 pp. 6000-6010
  55. Cardie, C., Farina, C., Bruce, T., Wagner, E. 2006. Using natural language processing to improve eRulemaking. In Proceedings of Digital Government Research
  56. Kwon, N., Shulman, S., Hovy, E. 2006. Multidimensional text analysis for eRulemaking. In Proceedings of Digital Government Research. In Proceeding of the 2006 international conference on Digital government research, pp. 157-166
  57. Conrad, J.G. & Schilder, F. 2001. Opinion mining in legal blogs. In Proceedings of the International Conference on Artificial Intelligence and Law (ICAIL), pp. 231-236, New York, NY, USA, 74 ACM
  58. Cao, J., Zeng. K., Wang. H., Cheng, J., Qiao, F., Wen, D., Gao, Y. 2014. Web-Based Traffic Sentiment Analysis: Methods and Applications. ITS(15) 2, April 2014, pp. 844-853
  59. Schumaker, R.P., Zhang, Y., Huang, C.N., Chen, H. 2012. Evaluating sentiment in financial news articles”. Decision Support Systems 53 pp. 458-464.
Index Terms

Computer Science
Information Sciences


Sentiment analysis Social Media Machine-learning approach Lexicon-based approach Sentiment classification