Sentiment Analysis of Big Data: Methods, Applications, and Open Challenges

Shayaa, Shahid and Jaafar, Noor Ismawati and Bahri, Shamshul and Ainin, Sulaiman and Phoong, Seuk Wai and Yeong, Wai Chung and Piprani, Arsalan Zahid and Al-Garadi, Mohammed Ali (2018) Sentiment Analysis of Big Data: Methods, Applications, and Open Challenges. IEEE Access, 6. pp. 37807-37827. ISSN 2169-3536

Full text not available from this repository.
Official URL: https://doi.org/10.1109/ACCESS.2018.2851311

Abstract

The development of IoT technologies and the massive admiration and acceptance of social media tools and applications, new doors of opportunity have been opened for using data analytics in gaining meaningful insights from unstructured information. The application of opinion mining and sentiment analysis (OMSA) in the era of big data have been used a useful way in categorizing the opinion into different sentiment and in general evaluating the mood of the public. Moreover, different techniques of OMSA have been developed over the years in different data sets and applied to various experimental settings. In this regard, this paper presents a comprehensive systematic literature review, aims to discuss both technical aspect of OMSA (techniques and types) and non-technical aspect in the form of application areas are discussed. Furthermore, this paper also highlighted both technical aspects of OMSA in the form of challenges in the development of its technique and non-technical challenges mainly based on its application. These challenges are presented as a future direction for research.

Item Type: Article
Uncontrolled Keywords: applications; big data; online social network; Opinion mining; opinionated data; sentiment analysis; social media
Subjects: H Social Sciences > HF Commerce
H Social Sciences > HF Commerce > Business
Divisions: Faculty of Business and Accountancy > Dept of Operations and Management Information Systems
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 13 Mar 2019 04:16
Last Modified: 13 Mar 2019 04:16
URI: http://eprints.um.edu.my/id/eprint/20689

Actions (login required)

View Item View Item