UPM Institutional Repository

Sarcastic tweets detection based on sentiment hashtags analysis


Citation

Nadali, Samaneh and Azmi Murad, Masrah Azrifah and Mohd Sharef, Nurfadhlina (2018) Sarcastic tweets detection based on sentiment hashtags analysis. Advanced Science Letters, 24 (2). pp. 1362-1365. ISSN 1936-6612; ESSN: 1936-7317

Abstract

Determining sarcasm among social media is one of the propounded problems from early days. Since recognizing sarcasm is important for development of sentiment analysis systems, identifying sarcastic tweets becomes an issue in this article. Due to the intentional ambiguity in sarcasm, few works have been done in sarcasm detection. In this article, a new Sarcastic Tweets Detection (STD) method is presented for identifying sarcastic tweets more accurate at the level of hashtag. In the proposed STD, Sarcasm Hashtags Classifier (SHC) is developed for classifying tweets into sarcasm and non-sarcasm based on the sentiment analysis of the hashtags. The SHC, works based on the Sarcasm Hashtags Indicator (SHI) and contrast between the orientation of the tweets and hashtag(s). The proposed classifier (SHC) helps us to interpret sarcastic better than the existing work by covering several types of tweets.


Download File

[img]
Preview
Text (Abstract)
Sarcastic tweets detection based on sentiment hashtags analysis.pdf

Download (33kB) | Preview

Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1166/asl.2018.10750
Publisher: American Scientific Publishers
Keywords: Hashtags sentiment analysis; Sarcastic detection; Sentiment analysis
Depositing User: Nabilah Mustapa
Date Deposited: 13 Aug 2018 03:45
Last Modified: 13 Aug 2018 03:45
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1166/asl.2018.10750
URI: http://psasir.upm.edu.my/id/eprint/64663
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item