publication . Article . Part of book or chapter of book . 2020

Using Twitter Streams for Opinion Mining: a case study on Airport Noise

Catherine Lavandier; Iheb Meddeb; Dimitris Kotzinos;
Open Access English
  • Published: 27 Mar 2020
  • Publisher: HAL CCSD
  • Country: France
Abstract
International audience; This paper proposes a classification model for opinion mining around airport noise based on techniques such as event detection and sentiment analysis applied on Twitter posts. Tweets are retrieved using the Twitter API either because of location or content.A dataset of preprocessed, with NLP techniques, tweets is manually annotated and then used to train an SVM (Support Vector Machine) classifier in order to extract the relevant ones from the obtained collections. The extracted tweets from the SVM classifier are fed to a lexicon-based classifier to filter out the false relevant and to increase precision. A lexicon-based sentiment classifi...
Subjects
ACM Computing Classification System: InformationSystems_MISCELLANEOUSInformationSystems_INFORMATIONSTORAGEANDRETRIEVALComputingMethodologies_PATTERNRECOGNITION
free text keywords: Twitter, Opinion mining, Natural Language Processing, Machine Learning, Sentiment analysis, Text mining, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], Transport engineering, STREAMS, Airport noise, Computer science
Funded by
EC| ANIMA
Project
ANIMA
Aviation Noise Impact Management through Novel Approaches
  • Funder: European Commission (EC)
  • Project Code: 769627
  • Funding stream: H2020 | RIA
Communities
Digital Humanities and Cultural Heritage
20 references, page 1 of 2

1. Statista. https://www.statista.com/statistics/282087/number-of-monthly-activeTwitter-users/, accessed: 2018-08-13

2. Tumblr. https://www.tumblr.com/, accessed: 2018-08-13

3. Twitter. https://Twitter.com/, accessed: 2018-08-13

4. 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. pp. 30-38. LSM '11, Association for Computational Linguistics, Stroudsburg, PA, USA (2011), http://dl.acm.org/citation.cfm?id=2021109.2021114

5. Asghar, M.Z., Khan, A., Ahmad, S., Qasim, M., Khan, I.A.: Lexiconenhanced sentiment analysis framework using rule-based classification scheme. PLOS ONE 12(2), 1-22 (02 2017). https://doi.org/10.1371/journal.pone.0171649, https://doi.org/10.1371/journal.pone.0171649

6. Authority, C.A.: Heathrow airport 2016 summer noise contours and noise action plan. Tech. rep. (2017)

7. Barbot, B., Lavandier, C., Chemine, P.: Liguistic analysis of field surveys carried out around two french airports. Tech. rep. (2007)

8. Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273-297 (1995)

9. Farooq, U., Mansoor, H., Nongaillard, A., Ouzrout, Y., Qadir, M.A.: Negation handling in sentiment analysis at sentence level. JCP 12(5), 470-478 (2017)

10. Harry, Z.: The optimality of naive bayes. In: Proceedings of Florida Artificial Intelligence Research Society Conference (FLAIRS). pp. 562-567. AAAI Press (2004)

11. Hutto, C.J., Gilbert, E.: VADER: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media. The AAAI Press (2014)

12. Jonathon, R.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: ACL the Association for Computer Liguistics. pp. 43-48 (2005) [OpenAIRE]

13. Khan, F.H., Bashir, S., Qamar, U.: TOM: twitter opinion mining framework using hybrid classification scheme. Decision Support Systems 57, 245-257 (2014)

14. Kouloumpis, E., Wilson, T., Moore, J.D.: Twitter sentiment analysis: The good the bad and the omg! In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. The AAAI Press, Barcelona, Catalonia, Spain (2011) [OpenAIRE]

15. Liu, B., Hu, M., Cheng, J.: Opinion observer: Analyzing and comparing opinions on the web. In: Proceedings of the 14th International World Wide Web conference (WWW-2005). ACM, Chiba, Japan (2005)

20 references, page 1 of 2
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