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
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...
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
Aviation Noise Impact Management through Novel Approaches
  • Funder: European Commission (EC)
  • Project Code: 769627
  • Funding stream: H2020 | RIA
Digital Humanities and Cultural Heritage
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