Real-Time Sentiment Analysis of Twitter Streaming data for Stock Prediction

Sushree Das · Ranjan Kumar Behera · Mukesh Kumar · Santanu Kumar Rath

127 citations0/0 files storedProcedia Computer Science

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Sentiment Analysis and Opinion Mining

Abstract

In this study, an attempt has been made for making financial decisions such as stock market prediction, to predict the potential prices of a company’s stock and to serve the need of this, Twitter data 1 2 has been considered for scoring the impression that is carried for a particular firm. Streaming data proves to be a perennial source of data analysis collected in real-time. Streaming data basically deals with the continuous flow of data which carries information from sources like websites, mobile phone applications, server logs, social websites, trading floors, etc. The major characteristics of such data being its accessibility and availability, help in proper analysis and prediction of user behavior in a ceaseless manner. The classifying model made out of historical data can be relentlessly honed to give even more accurate results since its outcome is always compared to the next tick of the clock. Spark streaming has been considered for the processing of humongous data and data ingestion tools like Twitter API and Apache Flume have been further implemented for analysis.

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References

2003The Efficient Market Hypothesis and Its Critics 2015Social big data: Recent achievements and new challenges 2012Big Data analytics 2011Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”2007Assessing the Impact of Media Guidelines for Reporting on Suicides in Austria: Interrupted time Series Analysis 2015Big Data Analysis: Apache Spark Perspective 2014Predicting political preference of Twitter users

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