Abstract

This paper comprehensively studies the sentiment analysis of stock market news and explains the maturity of sentiment analysis in the stock market scenario. I explain in its entirety the procedure, difficulties, and limitations concerning sentiment analysis of financial news to predict stocks. The stock market movements are regarded as highly unpredictable and a large number of factors contribute to that unpredictability. Factors such as market sentiment, government policies and company announcements are some of the major contributing factors however the list is not exhaustive. Technological advancements over the past two decades has enabled researchers and market professionals to develop mathematical models to optimize their returns and keep the risk in check. These advancements have given way to social media platforms especially Twitter to more conveniently express opinions and reviews. Narrowing it only to the stock market and financial scenario, Twitter is an attractive platform for the user community to discuss company health, company announcements, major news, and government policies etc to name a few. Companies or organizations in turn also boast their success on Twitter. This entire process of sharing news and opinions yields a large amount of financial data to be searched for an overall sentiment or a possible prediction of the stocks. This paper attempts to dive deep into the specifics of procedure, the current stage of its maturity, and the importance of Machine Learning in finance and the stock market.

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