Sentiment Analysis And Stock Market Prediction

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This chapter discusses about the important work and procedures related to sentiment analysis and stock market prediction done previously. These researches and publications are related to my speculations and will further motivate with the end-goal.
The approach used in this thesis is inspired by Bollen et al’s strategy [12], with a step taken forward to implement PageRank algorithm to increase the accuracy of results and use of different sentiment analysis techniques than the techniques used by him. In 2010, Bollen used Twitter data for finding the predictability of Twitter sentiments on stock market with high accuracy. He proposed a method for prediction of the changes in the stock market price based on the mood of people on Twitter.
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The second phase includes polarity classification in which corpus is classified into positive, negative, both or neutral using 10 different features and 2 simple classifiers. Neutral-polar classification and polarity classification achieved an average accuracy of 75.9% and 65.7%.
Besides understanding the effects of twitter data and sentiment analysis, a large group of researchers is attracted towards various types of predictions and their applications in various fields. Andrei Oghina [28] predicted Internet Movie Database (IMDB) ratings by information retrieval from social media. They hypothesized that the correlation can be found between the IMDB ratings and social media signals related to movie artifacts (e.g. movie title) . In 2010, Andranik et. al. [29 ] analyzed tweets which mentioned political parties and politician during German election 2009 and found that large number of tweets reflects voter preferences. Also, there are researches speculating the fact that Twitter can be used in areas like real-time event detection like earthquakes [30], tracking the spread of diseases [31].
There is also prior work in finding the correlations between social media and stock market. In 2010, Gilbert and Karahalios [32] estimated anxiety and fear from a dataset of over 20 million posts from LiveJournal site. They calculated an index of US national mood known as the Anxiety Index. Results of this experiment showed that when the Anxiety Index rose, the S&P 500 market ended
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