Opinion Mining of Twitter Events using Supervised Learning

Nida Hakak and Mahira Kirmani
Additional contact information
Nida Hakak: Maharshi Dayanand University, Haryana, India
Mahira Kirmani: Maharshi Dayanand University, Haryana, India

International Journal of Synthetic Emotions (IJSE), 2018, vol. 9, issue 2, 23-36

Abstract: Micro-blogs are a powerful tool to express an opinion. Twitter is one of the fastest growing micro-blogs and has more than 900 million users. Twitter is a rich source of opinion as users share their daily experience of life and respond to specific events using tweets on twitter. In this article, an automatic opinion classifier capable of automatically classifying tweets into different opinions expressed by them is developed. Also, a manually annotated corpus for opinion mining to be used by supervised learning algorithms is designed. An opinion classifier uses semantic, lexical, domain dependent, and context features for classification. Results obtained confirm competitive performance and the robustness of the system. Classifier accuracy is more than 75.05%, which is higher than the baseline accuracy.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 4018/IJSE.2018070102 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jse000:v:9:y:2018:i:2:p:23-36

Access Statistics for this article

International Journal of Synthetic Emotions (IJSE) is currently edited by João M. F. Rodrigues

More articles in International Journal of Synthetic Emotions (IJSE) from IGI Global
Bibliographic data for series maintained by Journal Editor ().