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

• Main Authors: Shayaa, Shahid, Jaafar, Noor Ismawati, Bahri, Shamshul, Ainin, Sulaiman, Phoong, Seuk Wai, Yeong, Wai Chung, Piprani, Arsalan Zahid, Al-Garadi, Mohammed Ali
• Format: Article
• Published: Institute of Electrical and Electronics Engineers 2018
• Subjects: HF Commerce; Business
• Online Access: http://eprints.um.edu.my/20689/; https://doi.org/10.1109/ACCESS.2018.2851311

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.