Skip to main content
SpringerLink
Log in

Using frame-based resources for sentiment analysis within the financial domain

  • Regular Paper
  • Published:
Progress in Artificial Intelligence Aims and scope Submit manuscript

Abstract

User-generated data in blogs and social networks have recently become a valuable resource for sentiment analysis in the financial domain, since they have been shown to be extremely significant to marketing research companies and public opinion organizations. In order to identify bullish and bearish sentiments associated with companies and stocks, we propose a fine-grained approach that returns a continuous score in the \([-\,1,+\,1]\) range. Our supervised approach leverages a frame-based ontological resource which produces feature sets such as lexical features, semantic features and their combination. One of the outcome of our analysis suggests that the frame-based ontological resource we have used might be successfully applied for sentiment analysis within the financial domain achieving better results than traditional sentiment analysis methods that do not embody semantics. We also show the higher performance of a fine-grained approach based solely on the evaluation of specific substrings of the message, rather than on features extracted from the whole text of a financial microblog message through the frame-based ontological resource. We have also compared our system with semi-supervised and unsupervised approaches and results indicate that our approach outperforms the others. Last but not the least, our approach is general and can be applied on top of any existing supervised method of polarity detection.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. https://twitter.com/.

  2. http://stocktwits.com/.

  3. http://alt.qcri.org/semeval2017/task5/.

  4. http://framester.com/.

  5. https://framenet.icsi.berkeley.edu/.

  6. https://wordnet.princeton.edu/.

  7. https://verbs.colorado.edu/~mpalmer/projects/verbnet.html.

  8. http://babelnet.org/.

  9. http://wiki.dbpedia.org/.

  10. http://www.yago-knowledge.org/.

  11. http://www.loa.istc.cnr.it/old/DOLCE.html.

  12. http://stanfordnlp.github.io/CoreNLP/.

  13. http://stanfordnlp.github.io/CoreNLP/api.html.

  14. https://spark.apache.org/.

  15. https://spark.apache.org/docs/1.6.0/sql-programming-guide.html.

  16. https://spark.apache.org/docs/1.6.0/mllib-guide.html.

  17. https://spark.apache.org/docs/1.6.0/graphx-programming-guide.html.

  18. https://spark.apache.org/docs/1.6.0/streaming-programming-guide.html.

  19. http://www.cs.waikato.ac.nz/ml/weka/.

  20. https://spark.apache.org/docs/latest/api/python/pyspark.html?highlight=pipe#pyspark.RDD.pipe.

  21. http://sentiwordnet.isti.cnr.it/.

  22. https://hlt-nlp.fbk.eu/technologies/sentiwords.

  23. https://github.com/stanfordnlp/CoreNLP/blob/master/data/edu/stanford/nlp/patterns/surface/stopwords.txt.

  24. https://spark.apache.org/docs/1.5.1/api/java/org/apache/spark/ml/feature/NGram.html.

  25. https://github.com/framester/Framester/wiki/Framester-Documentation.

  26. http://www.wikipedia.org/.

  27. http://kernelsvm.tripod.com/.

References

  1. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data. In: Proceedings of the Workshop on Languages in Social Media, LSM ’11, pp. 30–38. Association for Computational Linguistics, Stroudsburg, PA, USA (2011). http://dl.acm.org/citation.cfm?id=2021109.2021114. Accessed 25 Jan 2018

  2. Allan, K.: Natural Language Semantics. Wiley, London (2001)

    Google Scholar 

  3. Atzeni, M., Dridi, A., Recupero, D.R.: Fine-grained sentiment analysis on financial microblogs and news headlines. In: Dragoni, M., Solanki, M., Blomqvist, E. (eds.) Semantic Web Challenges-4th SemWebEval Challenge at ESWC 2017, Portoroz, Slovenia, May 28–June 1, 2017, Revised Selected Papers, Communications in Computer and Information Science, vol. 769, pp. 124–128. Springer (2017). https://doi.org/10.1007/978-3-319-69146-6_11

    Chapter  Google Scholar 

  4. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: A Nucleus for a web of open data. In: Aberer, K., Choi, K.S., Noy, N., Allemang, D., Lee, K.I., Nixon, L., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) The Semantic Web: 6th International Semantic Web Conference, Proceedings of the 2nd Asian Semantic Web Conference, ISWC 2007 + ASWC 2007, Busan, Korea, 11–15 November 2007, pp. 722–735. Springer, Berlin (2007). https://doi.org/10.1007/978-3-540-76298-0_52

    Chapter  Google Scholar 

  5. Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: Calzolari, N., Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., Tapias, D. (eds.) Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10), pp. 2200–2204. European Language Resources Association (ELRA), Valletta, Malta (2010)

  6. Baker, C.F., Fillmore, C.J., Lowe, J.B.: The Berkeley FrameNet project. In: Proceedings of the 17th International Conference on Computational Linguistics, vol. 1, COLING ’98, pp. 86–90. Association for Computational Linguistics, Stroudsburg, PA, USA (1998). https://doi.org/10.3115/980451.980860

  7. Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, COLING ’10, pp. 36–44. Association for Computational Linguistics, Stroudsburg, PA, USA (2010). http://dl.acm.org/citation.cfm?id=1944566.1944571

  8. Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012). https://doi.org/10.1145/2133806.2133826

    Article  Google Scholar 

  9. Cambria, E., Havasi, C., Hussain, A.: SenticNet 2: a semantic and affective resource for opinion mining and sentiment analysis. In: Youngblood, G.M., McCarthy, P.M. (eds.) Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference, pp. 202–207. AAAI Press (2012). http://dblp.uni-trier.de/db/conf/flairs/flairs2012.html#CambriaHH12. Accessed 29 Jan 2018

  10. Cambria, E., Hussain, A.: Sentic Computing: Techniques, Tools, and Applications. Springer, Berlin (2012)

    Book  Google Scholar 

  11. Cambria, E., Speer, R., Havasi, C., Hussain, A.: SenticNet: a publicly available semantic resource for opinion mining. In: AAAI Fall Symposium: Commonsense Knowledge, AAAI Technical Report, vol. FS-10-02, pp. 14–18. AAAI (2010). http://dblp.uni-trier.de/db/conf/aaaifs/aaaifs2010-02.html#CambriaSHH10. Accessed 5 Mar 2018

  12. Cortis, K., Freitas, A., Daudert, T., Huerlimann, M., Zarrouk, M., Handschuh, S., Davis, B.: Semeval-2017 task 5: Fine-grained sentiment analysis on financial microblogs and news. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 519–535 (2017)

  13. da Silva, N.F., Hruschka, E.R., Hruschka, E.R.: Tweet sentiment analysis with classifier ensembles. Decis. Support Syst. 66(C), 170–179 (2014). https://doi.org/10.1016/j.dss.2014.07.003

    Article  Google Scholar 

  14. Dessì, D., Recupero, D.R., Fenu, G., Consoli, S.: Exploiting cognitive computing and frame semantic features for biomedical document clustering. In: Proceedings of the Workshop on Semantic Web Solutions for Large-scale Biomedical Data Analytics co-located with 14th Extended Semantic Web Conference, SeWeBMeDA@ESWC 2017, Portoroz, Slovenia, 28 May 2017, pp. 20–34 (2017). http://ceur-ws.org/Vol-1948/paper3.pdf. Accessed 7 Feb 2018

  15. Devitt, A., Ahmad, K.: Sentiment polarity identification in financial news: a cohesion-based approach, pp. 984–991. Association for Computational Linguistics, Prague, CZ (2007). https://www.microsoft.com/en-us/research/publication/sentiment-polarity-identification-in-financial-news-a-cohesion-based-approach/

  16. Di Rosa, E., Durante, A.: Evaluating industrial and research sentiment analysis engines on multiple sources. In: Esposito, F., Basili, R., Ferilli, S., Lisi, F.A. (eds.) AI*IA 2017 Advances in Artificial Intelligence, pp. 141–155. Springer, Cham (2017)

    Chapter  Google Scholar 

  17. Dragoni, M., Recupero, D.R.: Challenge on fine-grained sentiment analysis within ESWC2016. In: Semantic Web Challenges-Third SemWebEval Challenge at ESWC 2016, Heraklion, Crete, Greece, May 29-June 2, 2016, Revised Selected Papers, pp. 79–94 (2016). https://doi.org/10.1007/978-3-319-46565-4_6

    Chapter  Google Scholar 

  18. Dragoni, M., Recupero, D.R. (eds.): Proceedings of the 3rd International Workshop at ESWC on Emotions, Modality, Sentiment Analysis and the Semantic Web Co-located with 14th ESWC 2017, Portroz, Slovenia, 28 May 2017, CEUR Workshop Proceedings, vol. 1874. CEUR-WS.org (2017). http://ceur-ws.org/Vol-1874

  19. Dragoni, M., Recupero, D.R., Denecke, K., Deng, Y., Declerck, T. (eds.): Joint Proceedings of the 2th Workshop on Emotions, Modality, Sentiment Analysis and the Semantic Web and the 1st International Workshop on Extraction and Processing of Rich Semantics from Medical Texts co-located with ESWC 2016, Heraklion, Greece, 29 May 2016, CEUR Workshop Proceedings, vol. 1613. CEUR-WS.org (2016). http://ceur-ws.org/Vol-1613

  20. Drake, A., Ringger, E.K., Ventura, D.: Sentiment regression: using real-valued scores to summarize overall document sentiment. In: Proceedings of the 2th IEEE International Conference on Semantic Computing (ICSC 2008), pp. 152–157. Santa Clara, California, USA, 4–7 August 2008 (2008). https://doi.org/10.1109/ICSC.2008.67

  21. Dridi, A., Atzeni, M., Recupero, D.R.: Bearish-bullish sentiment analysis on financial microblogs. In: Dragoni, M., Recupero, D.R. (eds.) Proceedings of the 3rd International Workshop at ESWC on Emotions, Modality, Sentiment Analysis and the Semantic Web Co-located with 14th ESWC 2017, Portroz, Slovenia, 28 May 2017, CEUR Workshop Proceedings, vol. 1874. CEUR-WS.org (2017). http://ceur-ws.org/Vol-1874/paper_2.pdf

  22. Dridi, A., Atzeni, M., Reforgiato Recupero, D.: Finenews: fine-grained semantic sentiment analysis on financial microblogs and news. Int. J. Mach. Learn. Cybernet. (2018). https://doi.org/10.1007/s13042-018-0805-x

  23. Dridi, A., Reforgiato Recupero, D.: Leveraging semantics for sentiment polarity detection in social media. Int. J. Mach. Learn. Cybernet. (2017). https://doi.org/10.1007/s13042-017-0727-z

  24. Du, J., Xu, H., Huang, X.: Box office prediction based on microblog. Expert Syst. Appl. 41(4), 1680–1689 (2014). https://doi.org/10.1016/j.eswa.2013.08.065

    Article  Google Scholar 

  25. Federici, M., Dragoni, M.: A knowledge-based approach for aspect-based opinion mining. In: Sack, H., Dietze, S., Tordai, A., Lange, C. (eds.) Semantic Web Challenges, pp. 141–152. Springer, Cham (2016)

    Chapter  Google Scholar 

  26. Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  27. Feuerriegel, S., Ratku, A., Neumann, D.: Analysis of how underlying topics in financial news affect stock prices using latent dirichlet allocation. In: Proceedings of the 2016 49th Hawaii International Conference on System Sciences (HICSS), HICSS ’16, pp. 1072–1081. IEEE Computer Society, Washington, DC, USA (2016). https://doi.org/10.1109/HICSS.2016.137

  28. Fillmore, C.J.: Frame semantics and the nature of language. Ann. N. Y. Acad. Sci. Conf. Orig. Dev. Lang. Speech 280(1), 20–32 (1976)

    Article  Google Scholar 

  29. Gaillat, T., Zarrouk, M., Freitas, A., Davis, B.: The ssix corpus: a trilingual gold standard corpus for sentiment analysis in financial microblogs. In: Proceedings of the 11th Edition of the Language Resources and Evaluation Conference, May 7–12 Miyazaki (Japan) (2018)

  30. Gangemi, A.: What’s in a schema? A formal metamodel for ECG and FrameNet. In: Huang, C.R., Calzolari, N., Gangemi, A., Lenci, A., Oltramari, A., Prévot, L. (eds.) Ontology and the Lexicon: A Natural language Processing Perspective, Studies in Natural Language Processing, pp. 144–181. Cambridge University Press, Cambridge (2010)

  31. Gangemi, A., Alam, M., Asprino, L., Presutti, V., Recupero, D.R.: Framester: A Wide Coverage Linguistic Linked Data Hub. In: Proceedings of the Knowledge Engineering and Knowledge Management-20th International Conference, EKAW 2016, Bologna, Italy, 19–23 November 2016, pp. 239–254 (2016). https://doi.org/10.1007/978-3-319-49004-5_16

    Google Scholar 

  32. Gangemi, A., Alani, H., Nissim, M., Cambria, E., Recupero, D.R., Lanfranchi, V., Kauppinen, T. (eds.): Joint Proceedings of the 1th Workshop on Semantic Sentiment Analysis (SSA2014), and the Workshop on Social Media and Linked Data for Emergency Response (SMILE 2014) Co-located with 11th European Semantic Web Conference (ESWC 2014), Crete, Greece, May 25th, 2014, CEUR Workshop Proceedings, vol. 1329. CEUR-WS.org (2015). http://ceur-ws.org/Vol-1329

  33. Gangemi, A., Navigli, R., Velardi, P.: The OntoWordNet Project: Extension and Axiomatization of Conceptual Relations in WordNet. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) Proceedings of the On the Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2003, Catania, Sicily, Italy, 3–7 November 2003. Lecture Notes in Computer Science, vol. 2888, pp. 820–838. Springer, Berlin (2003). https://doi.org/10.1007/978-3-540-39964-3_52

    Chapter  Google Scholar 

  34. Gangemi, A., Presutti, V., Recupero, D.R.: Frame-based detection of opinion holders and topics: a model and a tool. IEEE Comput. Int. Mag. 9(1), 20–30 (2014). https://doi.org/10.1109/MCI.2013.2291688

    Article  Google Scholar 

  35. Gatti, L., Guerini, M., Turchi, M.: Sentiwords: deriving a high precision and high coverage lexicon for sentiment analysis. IEEE Trans. Affect. Comput. 7(4), 409–421 (2016). https://doi.org/10.1109/TAFFC.2015.2476456

    Article  Google Scholar 

  36. Ghiassi, M., Skinner, J., Zimbra, D.: Twitter brand sentiment analysis: a hybrid system using N-gram analysis and dynamic artificial neural network. Expert Syst. Appl. 40(16), 6266–6282 (2013). https://doi.org/10.1016/j.eswa.2013.05.057

    Article  Google Scholar 

  37. Ghosh, A., Li, G., Veale, T., Rosso, P., Shutova, E., Barnden, J., Reyes, A.: SemEval-2015 Task 11: sentiment analysis of figurative language in Twitter. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 470–478. Association for Computational Linguistics, Denver, Colorado (2015). http://www.aclweb.org/anthology/S15-2080

  38. Go, A., Bhayani, R., Huang, L.: Twitter Sentiment Classification using Distant Supervision. CS224N Project Report, Stanford University (2009)

  39. Goonatilake, R., Herath, S.: The volatility of the stock market and news. Int. Res. J. Finance Econ. 3(11), 53–66 (2007)

    Google Scholar 

  40. Kalyani, J., Bharathi, H.N., Jyothi, R.: Stock trend prediction using news sentiment analysis. CoRR abs/1607.01958 (2016). arXiv:1607.01958

  41. Khadjeh Nassirtoussi, A., Aghabozorgi, S., Ying Wah, T., Ngo, D.C.L.: Review: text mining for market prediction: a systematic review. Expert Syst. Appl. 41(16), 7653–7670 (2014). https://doi.org/10.1016/j.eswa.2014.06.009

    Article  Google Scholar 

  42. Khadjeh Nassirtoussi, A., Aghabozorgi, S., Ying Wah, T., Ngo, D.C.L.: Text mining of news-headlines for FOREX market prediction. Expert Syst. Appl. 42(1), 306–324 (2015). https://doi.org/10.1016/j.eswa.2014.08.004

    Article  Google Scholar 

  43. Kipper, K., Dang, H.T., Palmer, M.: Class-based construction of a verb lexicon. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, pp. 691–696. AAAI Press (2000). http://portal.acm.org/citation.cfm?id=721573. Accessed 1 Mar 2018

  44. Kouloumpis, E., Wilson, T., Moore, J.D.: Twitter sentiment analysis: the good the bad and the OMG! In: Adamic, L.A., Baeza-Yates, R.A., Counts, S. (eds.) Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, pp. 538–541. The AAAI Press (2011)

  45. Lando, P., Lapujade, A., Kassel, G., Frst, F.: Towards a general ontology of computer programs. In: Filipe, J., Shishkov, B., Helfert, M. (eds.) ICSOFT (PL/DPS/KE/MUSE), pp. 163–170. INSTICC Press (2007). http://dblp.uni-trier.de/db/conf/icsoft/icsoft2007-1.html#LandoLKF07. Accessed 11 Feb 2018

  46. Li, X., Xie, H., Chen, L., Wang, J., Deng, X.: News impact on stock price return via sentiment analysis. Knowl. Based Syst. 69(Supplement C), 14–23 (2014). https://doi.org/10.1016/j.knosys.2014.04.022

    Article  Google Scholar 

  47. Li, X., Xie, H., Song, Y., Zhu, S., Li, Q., Wang, F.L.: Does summarization help stock prediction? A news impact analysis. IEEE Intell. Syst. 30(3), 26–34 (2015). https://doi.org/10.1109/MIS.2015.1

    Article  Google Scholar 

  48. Liu, H., Singh, P.: ConceptNet: a practical commonsense reasoning tool-kit. BT Technol. J. 22(4), 211–226 (2004). https://doi.org/10.1023/B:BTTJ.0000047600.45421.6d

    Article  Google Scholar 

  49. Loughran, T., McDonald, B.: When Is a liability not a liability? textual analysis, dictionaries, and 10-Ks. J. Finance 66(1), 35–65 (2011). https://doi.org/10.1111/j.1540-6261.2010.01625.x

    Article  Google Scholar 

  50. Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, HLT ’11, pp. 142–150. Association for Computational Linguistics, Stroudsburg, PA, USA (2011). http://dl.acm.org/citation.cfm?id=2002472.2002491. Accessed 2 Mar 2018

  51. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, 22–27 June 2014, Baltimore, MD, USA, System Demonstrations, pp. 55–60 (2014). http://aclweb.org/anthology/P/P14/P14-5010.pdf

  52. Maynard, D., Funk, A.: Automatic detection of political opinions in tweets. In: Proceedings of the 8th International Conference on The Semantic Web, ESWC’11, pp. 88–99. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-25953-1_8

    Google Scholar 

  53. Momtazi, S.: Fine-grained German sentiment analysis on social media. In: Proceedings of the Eighth International Conference on Language Resources and Evaluation, LREC 2012, Istanbul, Turkey, 23–25 May 2012, pp. 1215–1220 (2012). http://www.lrec-conf.org/proceedings/lrec2012/summaries/999.html

  54. Mostafa, M.M.: More than words: social networks’ text mining for consumer brand sentiments. Expert Syst. Appl. 40(10), 4241–4251 (2013). https://doi.org/10.1016/j.eswa.2013.01.019

    Article  Google Scholar 

  55. Mukherjee, S., Bhattacharyya, P.: Wikisent: Weakly supervised sentiment analysis through extractive summarization with wikipedia. In: Proceedings of the 2012th European Conference on Machine Learning and Knowledge Discovery in Databases, vol. Part I, ECMLPKDD’12, pp. 774–793. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-33460-3_55

    Chapter  Google Scholar 

  56. Navigli, R., Ponzetto, S.P.: BabelNet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell. 193, 217–250 (2012). https://doi.org/10.1016/j.artint.2012.07.001

    Article  MathSciNet  MATH  Google Scholar 

  57. O’Hare, N., Davy, M., Bermingham, A., Ferguson, P., Sheridan, P., Gurrin, C., Smeaton, A.F.: Topic-dependent sentiment analysis of financial blogs. In: Proceedings of the 1st International CIKM Workshop on Topic-sentiment Analysis for Mass Opinion, TSA ’09, pp. 9–16. ACM, New York, NY, USA (2009). https://doi.org/10.1145/1651461.1651464

  58. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Calzolari, N., Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., Tapias, D. (eds.) Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10), vol. 10, pp. 1320–1326. European Language Resources Association (ELRA), Valletta, Malta (2010)

  59. Paul, F., Neil, O., Michael, D., Adam, B., Scott, T., Paraic, S., Cathal, G., Alan, F.S.: Exploring the use of Paragraph-level annotations for sentiment analysis of financial blogs. In: Proceedings of the 1st Workshop on Opinion Mining and Sentiment Analysis (WOMSA 2009), WOMSA 2009, pp. 42–52 (2009)

  60. Pouransari, H., Ghili, S.: Deep learning for sentiment analysis of movie reviews. Technical Report, Stanford University (2014). https://cs224d.stanford.edu/reports/PouransariHadi.pdf. Accessed 5 Feb 2018

  61. Raina, P.: Sentiment analysis in news articles using sentic computing. In: Proceedings of the 2013 IEEE 13th International Conference on Data Mining Workshops, ICDMW ’13, pp. 959–962. IEEE Computer Society, Washington, DC, USA (2013). https://doi.org/10.1109/ICDMW.2013.27

  62. Recupero, D.R., Cambria, E.: Eswc’14 challenge on concept-level sentiment analysis. In: Semantic Web Evaluation Challenge-SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, 25–29 May 2014, Revised Selected Papers, pp. 3–20 (2014). https://doi.org/10.1007/978-3-319-12024-9_1

    Google Scholar 

  63. Recupero, D.R., Cambria, E., Rosa, E.D.: Semantic sentiment analysis challenge at ESWC2017. In: Semantic Web Challenges-4th SemWebEval Challenge at ESWC 2017, Portoroz, Slovenia, May 28–June 1, 2017, Revised Selected Papers, pp. 109–123 (2017). https://doi.org/10.1007/978-3-319-69146-6_10

    Chapter  Google Scholar 

  64. Recupero, D.R., Consoli, S., Gangemi, A., Nuzzolese, A.G., Spampinato, D.: A semantic web based core engine to efficiently perform sentiment analysis. In: The Semantic Web: ESWC 2014 Satellite Events-ESWC 2014 Satellite Events, Anissaras, Crete, Greece, 25–29 May 2014, Revised Selected Papers, pp. 245–248 (2014). https://doi.org/10.1007/978-3-319-11955-7_28

    Google Scholar 

  65. Recupero, D.R., Dragoni, M., Buscaldi, D., Alam, M., Cambria, E. (eds.): Proceedings of 4th Workshop on Sentic Computing, Sentiment Analysis, Opinion Mining, and Emotion Detection (EMSASW 2018), Heraklion, Greece, 4 June 2018, CEUR Workshop Proceedings, vol. 2111. CEUR-WS.org (2018). http://ceur-ws.org/Vol-2111

  66. Recupero, D.R., Dragoni, M., Presutti, V.: ESWC 15 challenge on concept-level sentiment analysis. In: Semantic Web Evaluation Challenges-Second SemWebEval Challenge at ESWC 2015, Portorož, Slovenia, May 31–June 4, 2015, Revised Selected Papers, pp. 211–222 (2015). https://doi.org/10.1007/978-3-319-25518-7_18

    Chapter  Google Scholar 

  67. Recupero, D.R., Presutti, V., Consoli, S., Gangemi, A., Nuzzolese, A.G.: Sentilo: frame-based sentiment analysis. Cognit. Comput. 7(2), 211–225 (2015). https://doi.org/10.1007/s12559-014-9302-z

    Article  Google Scholar 

  68. Rothfels, J., Tibshirani, J.: Unsupervised sentiment classification of English movie reviews using automatic selection of positive and negative sentiment items. Technical Report, Stanford University (2010). http://nlp.stanford.edu/courses/cs224n/2010/reports/rothfels-jtibs.pdf

  69. Saif, H., Bashevoy, M., Taylor, S., Fernández, M., Alani, H.: SentiCircles: A platform for contextual and conceptual sentimen analysis. In: The Semantic Web-ESWC 2016 Satellite Events, Heraklion, Crete, Greece, May 29–June 2, 2016, Revised Selected Papers, pp. 140–145 (2016). https://doi.org/10.1007/978-3-319-47602-5_28

    Chapter  Google Scholar 

  70. Saif, H., He, Y., Fernandez, M., Alani, H.: Semantic patterns for sentiment analysis of Twitter. In: Mika, P., Tudorache, T., Bernstein, A., Welty, C., Knoblock, C., Vrandečić, D., Groth, P., Noy, N., Janowicz, K., Goble, C. (eds.) Proceedings of the Semantic Web–ISWC 2014: 13th International Semantic Web Conference, Part II, Riva del Garda, Italy, 19–23 October 2014, pp. 324–340. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11915-1_21

    Google Scholar 

  71. Saif, H., He, Y., Fernández, M., Alani, H.: Contextual semantics for sentiment analysis of Twitter. Inf. Process. Manage. 52(1), 5–19 (2016). https://doi.org/10.1016/j.ipm.2015.01.005

    Article  Google Scholar 

  72. Schulz, A., Thanh, T.D., Paulheim, H., Schweizer, I.: A Fine-grained sentiment analysis approach for detecting crisis related microposts. In: 10th Proceedings of the International Conference on Information Systems for Crisis Response and Management, Baden-Baden, Germany, 12–15 May 2013, pp. 846–851 (2013). http://idl.iscram.org/files/schulz/2013/927_Schulz_etal2013.pdf

  73. Smith, T.C., Frank, E.: Statistical Genomics: Methods and Protocols, Chap. Introducing Machine Learning Concepts with WEKA, pp. 353–378. Springer, New York (2016). https://doi.org/10.1007/978-1-4939-3578-9_17

    Google Scholar 

  74. Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631–1642. Association for Computational Linguistics, Seattle, Washington, USA (2013). http://www.aclweb.org/anthology/D13-1170

  75. Sprenger, T.O., Tumasjan, A., Sandner, P.G., Welpe, I.M.: Tweets and trades: the information content of stock microblogs. Eur. Financ. Manag. 20(5), 926–957 (2014). https://doi.org/10.1111/j.1468-036X.2013.12007.x

    Article  Google Scholar 

  76. Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: A core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, WWW ’07, pp. 697–706. ACM, New York, NY, USA (2007). https://doi.org/10.1145/1242572.1242667

  77. Sun, D.: A Large-Scale Linear Regression Sentiment Model. Technical Report, University of California, Berkeley (2012). https://ai2-s2-pdfs.s3.amazonaws.com/b1bb/5cee34a934af2e13c8a17f324909fe961ebf.pdf

  78. Sun, F., Belatreche, A., Coleman, S., McGinnity, T.M., Li, Y.: Pre-processing online financial text for sentiment classification: A natural language processing approach. In: 2014 IEEE Conference on Computational Intelligence for Financial Engineering Economics (CIFEr), pp. 122–129 (2014). https://doi.org/10.1109/CIFEr.2014.6924063

  79. Takala, P., Malo, P., Sinha, A., Ahlgren, O.: Gold-standard for topic-specific sentiment analysis of economic texts. In: Calzolari, N., Choukri, K., Declerck, T., Loftsson, H., Maegaard, B., Mariani, J., Moreno, A., Odijk, J., Piperidis, S. (eds.) Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), pp. 2152–2157. European Language Resources Association (ELRA), Reykjavik, Iceland (2014)

  80. Van de Kauter, M., Breesch, D., Hoste, V.: Fine-grained analysis of explicit and implicit sentiment in financial news articles. Expert Syst. Appl. 42(11), 4999–5010 (2015). https://doi.org/10.1016/j.eswa.2015.02.007

    Article  Google Scholar 

  81. Wan, Y., Gao, Q.: An ensemble sentiment classification system of twitter data for airline services analysis. In: IEEE International Conference on Data Mining Workshop, ICDMW 2015, Atlantic City, NJ, USA, 14–17 November 2015, pp. 1318–1325 (2015). https://doi.org/10.1109/ICDMW.2015.7

  82. Zagibalov, T., Carroll, J.: Automatic seed word selection for unsupervised sentiment classification of Chinese text. In: Proceedings of the 22nd International Conference on Computational Linguistics, vol. 1, COLING ’08, pp. 1073–1080. Association for Computational Linguistics, Stroudsburg, PA, USA (2008). http://dl.acm.org/citation.cfm?id=1599081.1599216. Accessed 25 Jan 2018

  83. Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of the 2Nd USENIX Conference on Hot Topics in Cloud Computing, HotCloud’10, pp. 10–10. USENIX Association, Berkeley, CA, USA (2010). http://dl.acm.org/citation.cfm?id=1863103.1863113. Accessed 27 Jan 2018

Download references

Acknowledgements

The authors gratefully acknowledge Sardinia Regional Government for the financial support (Convenzione triennale tra la Fondazione di Sardegna e gli Atenei Sardi Regione Sardegna L.R. 7/2007 annualità 2016 DGR 28/21 del 17.05.2016, CUP: F72F16003030002). This work has been supported by Sardinia Regional Government (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2014–2020-Axis IV Human Resources, Objective l.3, Line of Activity l.3.1.). Moreover, the research leading to these results has received funding from the European Union Horizon 2020 the Framework Programme for Research and Innovation (2014–2020) under Grant Agreement 643808 Project MARIO Managing active and healthy aging with use of caring service robots.

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Palazzo delle Scienze, Via Ospedale 72, 09124, Cagliari, Italy

    Mattia Atzeni, Amna Dridi & Diego Reforgiato Recupero

Authors
  1. Mattia Atzeni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amna Dridi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Diego Reforgiato Recupero
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Diego Reforgiato Recupero.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Atzeni, M., Dridi, A. & Reforgiato Recupero, D. Using frame-based resources for sentiment analysis within the financial domain. Prog Artif Intell 7, 273–294 (2018). https://doi.org/10.1007/s13748-018-0162-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13748-018-0162-8

Keywords

Search

Navigation