Oasis: Online analytic system for incivility detection and sentiment classification

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Incivility detection is an important task to identify offensive language on online social media platforms. Sentiment analysis is an essential task of natural language processing to identify the emotions of given sentences. In this demonstration, we propose an online processing system, called Oasis, to perform incivility detection and sentiment prediction for short texts (e.g., tweets). Oasis offers several useful features including the CNN-LSTM classification models, incivility detection and sentiment analysis for real-time tweets and user-interested queries, and personalized search of trending topics in Twitter.

Original languageEnglish
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
EditorsPanagiotis Papapetrou, Xueqi Cheng, Qing He
PublisherIEEE Computer Society
Pages1098-1101
Number of pages4
ISBN (Electronic)9781728146034
DOIs
Publication statusPublished - Nov 2019
Event19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 - Beijing, China
Duration: 8 Nov 201911 Nov 2019

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2019-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
Country/TerritoryChina
CityBeijing
Period8/11/1911/11/19

Scopus Subject Areas

  • Computer Science Applications
  • Software

User-Defined Keywords

  • Incivility detection
  • Sentiment analysis

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