Journal of Network and Information Security

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Sentiment Analysis of Stock Market Related Tweets


Affiliations
1 Department of Computer Science Viswajyothi College of Engineering and Technology, Vazhakulam, Kerala, India
     

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Nowadays people express and reveal their opinions through different digital medium. A thorough analysis of data from these digital medium like news, social media can provide insights that can help people/business in making a better decision. Let us consider the stock market. In stock market a short term investor can take good trading decision, if he is provided with current market trends and related information in a timely manner. A lot of information is available in internet via various digital media. The trader needs to consolidate the data before making a decision. This is a time consuming process. But technologies like data mining and natural language processing can automate the above process. Using the technologies mentioned above, a system can be developed which can consolidate the news items from various source and provide the user some insights. This insight can help the investor to understand current market trend, news related to a specific company and the public opinion about that particular news item. Hence the short term investor will be able to make a better decision in a timely manner. The proposed system includes, data collection, data pre-processing, natural language processing, sentiment analysis, data classification and data visualization.

Keywords

Data Classification, Machine Learning, Natural Language Processing, Sentiment Analysis, Social Media, Stock Market, Text Processing, Twitter Analysis.
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  • D. D. Wu, L. Zheng, and D. L. Olson, “A decision support approach for online stock forum sentiment analysis,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 44, no. 8, pp. 1077-1087, 2014.

  • B. G. Deshmukh, P. S. Jain, M. S. Patwardhan, and V. Kulkarni, “Spin offs in Indian Stock Market owing to Twitter sentiments, commodity prices and analyst recommendations,” Proceedings of the International Conference on Advances in Information Communication Technology & Computing, p. 77, August 2016.

  • S. S. Abdullah, M. S. Rahaman, and M. S. Rahman, “Analysis of stock market using text mining and natural language processing,” IEEE International Conference on Informatics, Electronics & Vision, 2013.

  • D. K. Tayal, S. Yadav, K. Gupta, B. Rajput, and K. Kumari, “Polarity detection of sarcastic political tweets,” IEEE International Conference in Computing for Sustainable Global Development (INDIA.Com), pp. 625-628, 2014.

  • K. D. Dave, and N. P. Desai, “A comprehensive study of classification techniques for sarcasm detection on textual data,” IEEE International Conference on Electrical, Electronics, and Optimization Techniques, pp. 1985-1991, 2016.

  • T. Hardeniya, and D. A. Borikar, “Dictionary based approach to sentiment analysis: A review,” Proceedings of Conference on National Conference on Resent Trends in Computer Science and Information Technology, 2016.

  • A. Waloszek, and W. Waloszek, “A model for describing and classifying sentiment analysis methods,” 2017.

  • G. A. A. J. Alkubaisi, S. S. Kamaruddin, and H. Husni, “Stock market classification model using sentiment analysis on twitter based on hybrid naive Bayes classifiers,” Computer and Information Science, vol. 11, no. 1, p. 52, 2018.


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  • Sentiment Analysis of Stock Market Related Tweets

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Authors

Anima Chandran
Department of Computer Science Viswajyothi College of Engineering and Technology, Vazhakulam, Kerala, India

Abstract


Nowadays people express and reveal their opinions through different digital medium. A thorough analysis of data from these digital medium like news, social media can provide insights that can help people/business in making a better decision. Let us consider the stock market. In stock market a short term investor can take good trading decision, if he is provided with current market trends and related information in a timely manner. A lot of information is available in internet via various digital media. The trader needs to consolidate the data before making a decision. This is a time consuming process. But technologies like data mining and natural language processing can automate the above process. Using the technologies mentioned above, a system can be developed which can consolidate the news items from various source and provide the user some insights. This insight can help the investor to understand current market trend, news related to a specific company and the public opinion about that particular news item. Hence the short term investor will be able to make a better decision in a timely manner. The proposed system includes, data collection, data pre-processing, natural language processing, sentiment analysis, data classification and data visualization.

Keywords


Data Classification, Machine Learning, Natural Language Processing, Sentiment Analysis, Social Media, Stock Market, Text Processing, Twitter Analysis.

References