@inproceedings{6475e776e4584bbeae2dd1abeffd5b67,
title = "Oasis: Online analytic system for incivility detection and sentiment classification",
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.",
keywords = "Incivility detection, Sentiment analysis",
author = "Leyu Liu and Xin Huang and Jianliang Xu and Celine Song",
note = "Funding Information: ACKNOWLEDGMENT This work is supported by the NSFC No. 61702435, RGC Nos. 12200917, 12244916, 12632816, C6030-18GF, and HKBU No. RC-IG-FNRA/17-18/04. REFERENCES; 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 ; Conference date: 08-11-2019 Through 11-11-2019",
year = "2019",
month = nov,
doi = "10.1109/ICDMW.2019.00162",
language = "English",
series = "IEEE International Conference on Data Mining Workshops, ICDMW",
publisher = "IEEE Computer Society",
pages = "1098--1101",
editor = "Panagiotis Papapetrou and Xueqi Cheng and Qing He",
booktitle = "Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019",
address = "United States",
}