Sentiment Analysis Using Machine Learning in Stock Market: A Bibliometric Visualization

Abstract

Sentiments, opinions and emotions are considered as the basis for various kinds of analysis and anticipations. Sentiment analysis as a concept of generating the polarity from written, visual and verbal content is burgeoning in the advanced technological era. The article aims to carry out a bibliometric visualization study that reviews the current status, worldwide collaboration, evolution and potential emergence of machine learning-based sentiment analysis in the area of financial market along with several other themes. A corpus of 610 research publications from the Web of Science database during 2008–2022 is the base for depicting the contribution of eminent authors, journals, countries and research themes. Outcomes of the study reveal the wide scope of the ‘Sentiment Analysis’ theme in the existing research world along with providing a direction for future studies which may embed several advanced topics. The theme-related publications are segmented into four clusters where the ‘sentiment analysis and predictions using machine learning’ reflects the future emergence with existing growth. This concept has widely spread in India, China and the USA. Potential researchers can implement this concept in the stock market and financial predictions, sarcasm detection and behavioural mapping as major contributions to the firms’ effective decisions.