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Unveiling factors influencing judgment variation in Sentiment Analysis with Natural Language Processing and Statistics (2405.12055v1)

Published 20 May 2024 in cs.CL

Abstract: TripAdvisor reviews and comparable data sources play an important role in many tasks in NLP, providing a data basis for the identification and classification of subjective judgments, such as hotel or restaurant reviews, into positive or negative polarities. This study explores three important factors influencing variation in crowdsourced polarity judgments, focusing on TripAdvisor reviews in Spanish. Three hypotheses are tested: the role of Part Of Speech (POS), the impact of sentiment words such as "tasty", and the influence of neutral words like "ok" on judgment variation. The study's methodology employs one-word titles, demonstrating their efficacy in studying polarity variation of words. Statistical tests on mean equality are performed on word groups of our interest. The results of this study reveal that adjectives in one-word titles tend to result in lower judgment variation compared to other word types or POS. Sentiment words contribute to lower judgment variation as well, emphasizing the significance of sentiment words in research on polarity judgments, and neutral words are associated with higher judgment variation as expected. However, these effects cannot be always reproduced in longer titles, which suggests that longer titles do not represent the best data source for testing the ambiguity of single words due to the influence on word polarity by other words like negation in longer titles. This empirical investigation contributes valuable insights into the factors influencing polarity variation of words, providing a foundation for NLP practitioners that aim to capture and predict polarity judgments in Spanish and for researchers that aim to understand factors influencing judgment variation.

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References (32)
  1. Survey on sentiment analysis: evolution of research methods and topics. Artificial Intelligence Review. 2023; p. 1–42.
  2. Universal, unsupervised (rule-based), uncovered sentiment analysis. Knowledge-Based Systems. 2017;118:45–55.
  3. Experimenting with UD Adaptation of an Unsupervised Rule-based Approach for Sentiment Analysis of Mexican Tourist Texts. In: CEUR Workshop Proceedings. vol. 3496; 2023. p. 216–225. Available from: http://www.grupolys.org/biblioteca/KelZamMatGom2023a.pdf.
  4. Hutto C, Gilbert E. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the international AAAI conference on web and social media. vol. 8; 2014. p. 216–225.
  5. Lasersohn P. Context dependence, disagreement, and predicates of personal taste. Linguistics and philosophy. 2005;28:643–686.
  6. Kennedy C. Two sources of subjectivity: Qualitative assessment and dimensional uncertainty. Inquiry. 2013;56(2-3):258–277.
  7. Umbach C. Evaluative Predicates: Beyond Fun and Tasty. The Wiley Blackwell Companion to Semantics. 2020; p. 1–31.
  8. Kennedy C, Willer M. Familiarity inferences, subjective attitudes and counterstance contingency: towards a pragmatic theory of subjective meaning. Linguistics and Philosophy. 2022;45(6):1395–1445.
  9. Kellert O. Free choice indefinites in old and modern Italian. Journal of Historical Syntax. 2021;5(1-13):1–27.
  10. Alonso-Ovalle L, Royer J. Random choice from likelihood: The case of chuj mayan. Journal of Semantics. 2021;38(4):483–529.
  11. Overview of rest-mex at iberlef 2023: Research on sentiment analysis task for mexican tourist texts. Procesamiento del Lenguaje Natural. 2023;71:425–436.
  12. Wu Y, Jin P. SemEval-2010 Task 18: Disambiguating Sentiment Ambiguous Adjectives. In: Proceedings of the 5th International Workshop on Semantic Evaluation. Uppsala, Sweden: Association for Computational Linguistics; 2010. p. 81–85. Available from: https://aclanthology.org/S10-1014.
  13. Word Polarity Disambiguation Using Bayesian Model and Opinion-Level Features. Cogn Comput. 2015;7:369–380. doi:DOI 10.1007/s12559-014-9298-4.
  14. Polarity variation of (sentiment) words: A new approach of measuring lexical ambiguity. Journal of Quantitative Linguistics. submitted;.
  15. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Burstein J, Doran C, Solorio T, editors. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis, Minnesota: Association for Computational Linguistics; 2019. p. 4171–4186. Available from: https://aclanthology.org/N19-1423.
  16. Efficient estimation of word representations in vector space. arXiv preprint arXiv:13013781. 2013;.
  17. Israel M. Polarity sensitivity as lexical semantics. Linguistics and philosophy. 1996; p. 619–666.
  18. Cross-Linguistic Sentiment Analysis: From English to Spanish. In: Proceedings of the International Conference RANLP-2009. Borovets, Bulgaria: Association for Computational Linguistics; 2009. p. 50–54. Available from: https://aclanthology.org/R09-1010.
  19. 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, et al., editors. Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10). Valletta, Malta: European Language Resources Association (ELRA); 2010.Available from: http://www.lrec-conf.org/proceedings/lrec2010/pdf/769_Paper.pdf.
  20. Sentiment analysis based on expanded aspect and polarity-ambiguous word lexicon. International Journal of Advanced Computer Science and Applications. 2015;6(2).
  21. A fuzzy computing model for identifying polarity of Chinese sentiment words. Computational intelligence and neuroscience. 2015;2015:47–47.
  22. The Construction of Sentiment Lexicon Based on Context-Dependent Part-of-Speech Chunks for Semantic Disambiguation. IEEE Access. 2020;8:63359–63367.
  23. Hill F, Korhonen A. Learning Abstract Concept Embeddings from Multi-Modal Data: Since You Probably Can’t See What I Mean. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar: Association for Computational Linguistics; 2014. p. 255–265. Available from: https://aclanthology.org/D14-1032.
  24. Wiebe J. Learning Subjective Adjectives from Corpora. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence; 2000. p. 735–740.
  25. Coarse-grained+/-effect word sense disambiguation for implicit sentiment analysis. IEEE Transactions on Affective Computing. 2017;8(4):471–479.
  26. Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection. CoRR. 2018;abs/1809.08193.
  27. Stanza: A Python Natural Language Processing Toolkit for Many Human Languages. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Online: Association for Computational Linguistics; 2020. p. 101–108. Available from: https://aclanthology.org/2020.acl-demos.14.
  28. Student. The probable error of a mean. Biometrika. 1908; p. 1–25.
  29. Toward a Perspectivist Turn in Ground Truthing for Predictive Computing. CoRR. 2021;abs/2109.04270.
  30. Levene H. In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling. Stanford University Press. 1960; p. 278–292.
  31. BabelSenticNet: A Commonsense Reasoning Framework for Multilingual Sentiment Analysis. In: IEEE Symposium Series on Computational Intelligence, SSCI 2018, Bangalore, India, November 18-21, 2018. IEEE; 2018. p. 1292–1298. Available from: https://doi.org/10.1109/SSCI.2018.8628718.
  32. Faster and more Accurate Sentiment Analysis with Neural Sequence Labeling Parser. in preparation;.
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