Abstract
In this modern age, social media platforms like Twitter plays a crucial role of sharing public opinions about any ongoing events in the form of sentiment, which may have direct impact on Stock Market movement. Twitter API do not allow more than 7 day’s historical data to be downloaded. In this paper, python language is used to collect historical data with Geo-Tagging for region-wise analysis. Here tweets are categorized by positive as 1, negative as −1 and neutral as 0. Here historical geo-tagged labelled tweets are collected using keywords and used to notice the impact on stock market during an event or after a specific event. Stock Market is very dynamic and complex area to be able to study. Since Stock Market data is time series data, we use ARIMA model to forecast the market movement. We then make a comparative study of the results of ARIMA of univariate and VAR of multivariate time series where extra variable is the sentiment score generated from the Twitter sentiment analysis.
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Das, N., Ghosh, P., Roy, D. (2020). Effect of Demonetization on Stock Market Co-rrelated with Geo-Twitter Sentiment Analysis. In: Dawn, S., Balas, V., Esposito, A., Gope, S. (eds) Intelligent Techniques and Applications in Science and Technology. ICIMSAT 2019. Learning and Analytics in Intelligent Systems, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-42363-6_92
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DOI: https://doi.org/10.1007/978-3-030-42363-6_92
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