Skip to main content
SpringerLink
Log in

CSR & Sentiment Analysis: A New Customized Dictionary

  • Conference paper
  • First Online:
Deep Learning Theory and Applications (DeLTA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1875))

Included in the following conference series:

  • 538 Accesses

Abstract

Communication concerning the CSR pillars is key to sustainable corporate development. Sentiment analysis (SA) is a sub-area of natural language processing for studying communication through the classification of negative or positive opinions. Measuring sentiment is characterized by pitfalls related to: a) the context, where the polarity classification depends on the domain; b) the methods, if lexicon-based, machine learning, or their combination; c) the language, where the lack of resources (different from English) in literature was observed. Strategic communication based on CSR has no domain resources for investigating sentiment, neither in English nor in other languages. Our contribution is placed within the methodological setting of SA for the sustainability framework. We combined lexicon-based methods with machine-learning ones to build a customized lexicon for analyzing the CSR. The innovation concerns: 1) a domain corpus-based approach for improving a general pre-constructed dictionary; 2) the application for Italian; and 3) the performance assessment through machine learning. We developed an algorithm characterized by a multi-stage model that combines text analysis with network analysis and captures semantic concordances through an index of keyword content in the text. To validate our model from a machine learning perspective, we divided our data collection into five random samples: one sample was utilized as a train set or baseline for the lexicon’s implementation, and four were used as test sets. The study showed a notable increase in performance metrics across all samples, demonstrating the effectiveness of our proposal in building a customized lexicon for analyzing CSR in the Italian context.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • 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

References

  1. Aguinis, H., Glavas, A.: What we know and don’t know about corporate social responsibility: a review and research agenda. J. Manage. 38(4), 932–968 (2012)

    Google Scholar 

  2. Arnold, I., Vrugt, E.: Fundamental uncertainty and stock market volatility. Appl. Financ. Econ. 18(17), 1425–1440 (2008)

    Article  Google Scholar 

  3. 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., et al. (eds.) Proceedings of the International Conference on Language Resources and Evaluation, LREC 2010. European Language Resources Association (ELRA), Valletta, Malta (2010)

    Google Scholar 

  4. Carpenter, G., White, P.: Sustainable development: finding the real business case. Corp. Environ. Strat.: Int. J. Sustain. Bus. 11 (2), 51–56 (2004)

    Google Scholar 

  5. Catelli, R., Pelosi, S., Espostio, M.: Lexicon-based vs. BERT-based sentiment analysis: a comparative study in Italian. Electronics 11(3), 374 (2022)

    Google Scholar 

  6. Chandler, D., Werther, W.B.: Strategic Corporate Social Responsibility. Stakeholders, Globalization, and Sustainable Value Creation, 3ł ed. SAGE Publications Inc., Thousand Oaks, CA(2014)

    Google Scholar 

  7. Che, S., Li, X.: RETRACTED ARTICLE: HCI with DEEP learning for sentiment analysis of corporate social responsibility report. Curr. Psychol., 1–1 (2020). https://doi.org/10.1007/s12144-020-00789-y

  8. Chin, C.C., Tseng, Y.-D.: Quality evaluation of product reviews using an information quality framework. Decis. Support Syst. 50(4), 755–768 (2011). https://doi.org/10.1016/j.dss.2010.08.023

    Article  Google Scholar 

  9. Esposito, B., Sessa, M.R., Sica, D., Malandrino, O.: Exploring corporate social responsibility in the Italian wine sector through websites. TQM J. 33(7), 222–252 (2021)

    Article  Google Scholar 

  10. Esuli, A., Sebastiani, F.: SENTIWORDNET: a publicly available lexical resource for opinion mining. In: Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC 2006). European Language Resources Association (ELRA), Genoa, Italy (2006)

    Google Scholar 

  11. Freeman, L.C.: Centrality in social networks: conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)

    Article  Google Scholar 

  12. Gazzetta Ufficiale della Repubblica Italiana: Legislative Decree n. 254 (2016). https://www.gazzettaufficiale.it/eli/id/2017/01/10/17G00002/sg. Accessed Apr 2023

  13. Glavic, P., Lukman, R.: Review of sustainability terms and their definitions. J. Clean. Prod. 15(18), 1875–1885 (2007). https://doi.org/10.1016/j.jclepro.2006.12.006

    Article  Google Scholar 

  14. Henry, E.: Are investors influenced by how earnings press releases are written? J. Bus. Commun. 45(4), 363–407 (2008). https://doi.org/10.1177/0021943608319388

    Article  Google Scholar 

  15. Hilbert, M., Barnett, G., Blumenstock, J., Contractor, N., Diesner, J., Frey, S., et al.: Computational communication science: a methodological catalyzer for a maturing discipline. Int. J. Commun. 13, 3912–3934 (2019)

    Google Scholar 

  16. Hu, M., Liu, B.: Mining opinion features in customer reviews. In: Proceedings of the AAAI Conference on Artificial Intelligence, \(19^th\), pp. 755–760 (2004)

    Google Scholar 

  17. Hu, Y., Li, W.: Document sentiment classification by exploring description model of topical. Comput. Speech Lang. 25(2), 386–403 (2011). https://doi.org/10.1016/j.csl.2010.07.004

    Article  Google Scholar 

  18. Kang, H., Yoo, S.J., Han, D.: Senti-lexicon and improved Naive Bayes algorithms for sentiment analysis of restaurant reviews. Expert Syst. Appl. 39(5), 6000–6010 (2012). https://doi.org/10.1016/j.eswa.2011.11.107

    Article  Google Scholar 

  19. Kleinnijenhuis, J., Van Hoof, A.M., Van Atteveldt, W.: The combined effects of mass media and social media on political perceptions and preferences. J. Commun. 69(6), 650–673 (2019). https://doi.org/10.1093/joc/jqz038

    Article  Google Scholar 

  20. Lagasio, V., Cucari, N., Åberg, C.: How corporate social responsibility initiatives affect the choice of a bank: empirical evidence of Italian context. Corp. Soc. Responsib. Environ. Manage. 28(4), 1348–1359 (2021). https://doi.org/10.1002/csr.2162

    Article  Google Scholar 

  21. Lengauer, G., Esser, F., Berganza, R.: Negativity in political news: a review of concepts, operationalizations and key findings. Journalism: Theory Pract. Criticism 69(6), 179–202 (2012) https://doi.org/10.1177/1464884911427800

  22. Liu, B.: Sentiment analysis and subjectivity. In: Indurkhya, N. and Damerau, J. (eds.) Handbook of Natural Language Processing (Second Edition), pp. 627–666 (2010)

    Google Scholar 

  23. Loughran, T., Mcdonald, B.: When is a liability not a liability? textual analysis, dictionaries, and 10-Ks. J. Finan. 66(6), 35–65 (2011). https://doi.org/10.1111/j.1540-6261.2010.01625.x

    Article  Google Scholar 

  24. Margolin, D.B.: Computational contributions: a symbiotic approach to integrating big, observational data studies into the communication field. Commun. Methods Meas. 13(1), 229–247 (2019). https://doi.org/10.1080/19312458.2019.1639144

    Article  MathSciNet  Google Scholar 

  25. Matthes, J., Kohring, M.: The content analysis of media frames: toward improving reliability and validity. J. Commun. 58(2), 258–279 (2008). https://doi.org/10.1111/j.1460-2466.2008.00384.x

    Article  Google Scholar 

  26. McWilliams, A., Siegel, D.S.: Creating and capturing value: strategic corporate social responsibility, resource-based theory, and sustainable competitive advantage. J. Manage. 37(5), 1480–1495 (2011)

    Google Scholar 

  27. Meriton, X.: Domain independence of Machine Learning and lexicon based methods in sentiment analysis (2020). http://essay.utwente.nl/81995/. Accessed 4 Apr 2023

  28. Mohammad, S., Turney, P.: Emotions evoked by common words and phrases: using mechanical Turk to create an emotion lexicon. In: Proceedings of the NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 26–34. Association for Computational Linguistics, Los Angeles, CA (2010)

    Google Scholar 

  29. Morsing, M., Schultz, M.: Corporate social responsibility communication: stakeholder information, response and involvement strategies. Bus. Ethics: Eur. Rev. 15(42), 323–338 (2006). https://doi.org/10.1111/j.1467-8608.2006.00460.x

    Article  Google Scholar 

  30. Mućko, P.: Sentiment analysis of CSR disclosures in annual reports of EU companies. Procedia Comput. Sci. 192, 3351–3359 (2021). https://doi.org/10.1016/j.procs.2021.09.108

    Article  Google Scholar 

  31. Nielsen, F.A.: A new ANEW: evaluation of a word list for sentiment analysis in microblogs. In: Proceedings of the ESWC2011, Workshop on Making Sense of Microposts: Big Things Come in Small Packages, pp. 93-98 (2011) https://doi.org/10.48550/arXiv.1103.2903

  32. Ortigosa-Hernandez, J., Rodriguez, J.D., Alzate, L., et al.: Approaching sentiment analysis by using semi-supervised learning of multi-dimensional classifiers. Neurocomputing 92(90), 98–115 (2012). https://doi.org/10.1016/j.neucom.2012.01.030

    Article  Google Scholar 

  33. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 192, 1–135 (2008)

    Google Scholar 

  34. Pianta, E., Bentivogli, L. Girardi, C.: MultiWordNet: developing an aligned multilingual database. In: Proceedings of the 1\(^st\) International WordNet Conference, Mysore, India, pp. 293–302 (2002)

    Google Scholar 

  35. Plutchik, R.: A general psychoevolutionary theory of emotion. In: R. Plutchik & H. Kellerman (eds.) Emotion: Theory, Research and Experience. Theories of Emotion, pp. 3–33 (1980)

    Google Scholar 

  36. Rodgers, S., Thorson, E.: A socialization perspective on male and female reporting. J. Commun. 53(4), 658–675 (2003). https://doi.org/10.1111/j.1460-2466.2003.tb02916.x

    Article  Google Scholar 

  37. Shin, J., Thorson, K.: Partisan selective sharing: the biased diffusion of fact-checking messages on social media. J. Commun. 67(24), 233–255 (2017). https://doi.org/10.1111/jcom.12284

    Article  Google Scholar 

  38. Song, Y., Wang, H., Zhu, M.: Sustainable strategy for corporate governance based on the sentiment analysis of financial reports with CSR. Financ. Innov. 4(1), 1–14 (2018). https://doi.org/10.1186/s40854-018-0086-0

    Article  Google Scholar 

  39. Steurer, R., Langer, M.E., Konrad, A., Martinuzzi, A.: Corporations, stakeholders and sustainable development: a theoretical exploration of business-society relations. J. Bus. Ethics 61, 263–281 (2005)

    Article  Google Scholar 

  40. Tong, R.: An operational system for detecting and tracking opinions in on-line discussions. In: Working Notes of the ACM SIGIR 2001 Workshop on Operational Text Classification, pp. 1–6 (2001)

    Google Scholar 

  41. Turney, P.D., Littman, M.L.: Measuring praise and criticism: inference of semantic orientation from association. ACM Trans. Inf. Syst. 21(4), 315–346 (2003). https://doi.org/10.1145/944012.944013

    Article  Google Scholar 

  42. Van Atteveldt, W., Welbers, K., Van der Velden, M.A.C.G.: Studying political decision-making with automatic text analysis. In: Oxford Research Encyclopedia of Politics (2019). https://doi.org/10.1093/acrefore/9780190228637.013.957

  43. Vassallo, M., et al.: Polarity Imbalance in Lexicon-based sentiment analysis. In: Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020, Bologna (2020)

    Google Scholar 

  44. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IULM University, 20143, Milan, Italy

    Emma Zavarrone & Alessia Forciniti

Authors
  1. Emma Zavarrone
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alessia Forciniti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alessia Forciniti .

Editor information

Editors and Affiliations

  1. Université de Tours, Tours, France

    Donatello Conte

  2. Instituto de Telecomunicações and University of Lisbon, Lisbon, Portugal

    Ana Fred

  3. Ford Motor Company, Commerce Township, MI, USA

    Oleg Gusikhin

  4. University of Naples Federico II, Naples, Italy

    Carlo Sansone

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zavarrone, E., Forciniti, A. (2023). CSR & Sentiment Analysis: A New Customized Dictionary. In: Conte, D., Fred, A., Gusikhin, O., Sansone, C. (eds) Deep Learning Theory and Applications. DeLTA 2023. Communications in Computer and Information Science, vol 1875. Springer, Cham. https://doi.org/10.1007/978-3-031-39059-3_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-39059-3_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-39058-6

  • Online ISBN: 978-3-031-39059-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation