Title: Sentiment analysis of micro-blogging sites using supervised learning: a narrative review of recent studies
Authors: Akanksha Bisht; H.S. Bhadauria; Jitendra Virmani; Annapurna Singh; Kriti
Addresses: National Institute of Electronics and Information Technology, Haridwar, Uttarakhand, 249403, India ' Department of Computer Science and Engineering, GB Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand, 246194, India ' Council of Scientific and Industrial Research, Central Scientific Instruments Organization (CSIR-CSIO), Ministry of Science and Technology, Government of India, Sector 30-C, Chandigarh-160030, India ' Department of Computer Science and Engineering, GB Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand, 246194, India ' DIT University, Dehradun, Uttarakhand, 248009, India
Abstract: Sentiment analysis is a task of predicting sentiments from the opinionated data and classifies them as positive, negative, ratings (stars or numerical), thumbs up - thumbs down and so forth. In the present survey, we have covered numerous datasets, methodologies, developments together with indications for advances in the near future. Inspired by the achievements of deep learning, plenty of researchers are utilising the deep learning models for conducting sentiment analysis. Therefore, we have highlighted some studies regarding the use of machine learning and deep learning models on different micro-blogging sites with the evolution in sentiment analysis. The survey presents a number of suitable illustrations-most prominently, a table that summarises previous papers along different dimensions such as types of objectives, classification techniques and dataset used.
Keywords: sentiment analysis; NLP; machine learning; deep learning; text classification.
International Journal of Knowledge and Learning, 2022 Vol.15 No.2, pp.89 - 119
Accepted: 13 May 2021
Published online: 07 Apr 2022 *