Abstract
It is a well-known fact that sentiments play a vital role and is an incredibly influential tool in several aspects of human life. Sentiments also drive proactive business solutions. Studies have shown that the more appropriate data is gathered and analyzed at the right time, the higher the success of sentiment analysis. This paper analyses the correlation between the public mood and the variation in stock prices towards companies in different domains. For each tweet, scores are assigned to eight predefined moods namely “Joy”, “Sadness”, “Fear”, “Anger”, “Trust”, “Disgust”, “Surprise” and “Anticipation”. A regression model is applied to the mood scores and the stock prices dataset to obtain the R-squared score, which is a metric used to evaluate the model. The paper aims to find the moods that best reflect the stock values of the respective companies. From the results, it is observed that there is a definite correlation between public mood and stock market.
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Cowlessur, S.K., Annappa, B., Sree, B.K., Gupta, S., Velaga, C. (2019). Measuring the Influence of Moods on Stock Market Using Twitter Analysis. In: Satapathy, S., Bhateja, V., Somanah, R., Yang, XS., Senkerik, R. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 863. Springer, Singapore. https://doi.org/10.1007/978-981-13-3338-5_29
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DOI: https://doi.org/10.1007/978-981-13-3338-5_29
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