Microblog Sentiment Prediction based on User Past Content

Yassin Belhareth, Chiraz Latiri

2019

Abstract

Analyzing massive, noisy and short microblogs is a very challenging task where traditional sentiment analysis and classification methods are not easily applicable due to inherent characteristics such social media content. Sentiment analysis, also known as opinion mining, is a mechanism for understanding the natural disposition that people possess towards a specific topic. Therefore, it is very important to consider the user context that usually indicates that microblogs posted by the same person tend to have the same sentiment label. One of the main research issue is how to predict twitter sentiment as regards a topic on social media? In this paper, we propose a sentiment mining approach based on sentiment analysis and supervised machine learning principles to the tweets extracted from Twitter. The originality of the suggested approach is that classification does not rely on tweet text to detect polarity, but it depends on users’ past text content. Experimental validation is conducted on a tweet corpus taken from data of SemEval 2016. These tweets talk about several topics, and are annotated in advance at the level of sentiment polarity. We have collected the past tweets of each author of the collection tweets. As an initial experiment in the prediction of user sentiment on a topic, based on his past, the results obtained seem acceptable, and could be improved in future work.

Paper Citation

in Harvard Style

Belhareth Y. and Latiri C. (2019). Microblog Sentiment Prediction based on User Past Content.In Proceedings of the 15th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-386-5, pages 250-256. DOI: 10.5220/0008073102500256

in Bibtex Style

@conference{webist19,
author={Yassin Belhareth and Chiraz Latiri},
title={Microblog Sentiment Prediction based on User Past Content},
booktitle={Proceedings of the 15th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2019},
pages={250-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008073102500256},
isbn={978-989-758-386-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Microblog Sentiment Prediction based on User Past Content
SN - 978-989-758-386-5
AU - Belhareth Y.
AU - Latiri C.
PY - 2019
SP - 250
EP - 256
DO - 10.5220/0008073102500256