Stock Market Forecasting Techniques: Literature Survey

The goal of this paper is to study different techniques to predict stock price movement using the sentiment analysis from social media, data mining. In this paper we will find efficient method which can predict stock movement more accurately. Social media offers a powerful outlet for people’s thoughts and feelings it is an enormous ever-growing source of texts ranging from everyday observations to involved discussions. This paper contributes to the field of sentiment analysis, which aims to extract emotions and opinions from text. A basic goal is to classify text as expressing either positive or negative emotion. Sentiment classifiers have been built for social media text such as product reviews, blog posts, and even twitter messages. With increasing complexity of text sources and topics, it is time to re-examine the standard sentiment extraction approaches, and possibly to redefine and enrich the definition of sentiment. Next, unlike sentiment analysis research to date, we examine sentiment expression and polarity classification within and across various social media streams by building topical datasets within each stream. Different data mining methods are used to predict market more efficiently along with various hybrid approaches. We conclude that stock prediction is very complex task and various factors should be considered for forecasting the market more accurately and efficiently.