Abstract
Prediction of stock movements and share market has always remained an area of great curiosity and concern for investors. It has already been established that the movement of market shares a big correlation with the sentiments about it. In this paper, we have applied sentiments analysis techniques and machine learning principles to foretell the stock market trends of three major commodities, Gold, Silver and Crude oil. We have used the SentiWordNet library to quantify the emotions expressed in the text. Further neural network has been trained over the calculated readings. Thereafter, the trained neural network is used to forecast the future values. The efficacy of the proposed model is measured on the basis of mean absolute percentage error. The results clearly reflect that there in fact lies a strong correlation between public mood and stock market variations.
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Keshwani, K., Agarwal, P., Kumar, D., Ranvijay (2018). Prediction of Market Movement of Gold, Silver and Crude Oil Using Sentiment Analysis. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 554. Springer, Singapore. https://doi.org/10.1007/978-981-10-3773-3_11
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DOI: https://doi.org/10.1007/978-981-10-3773-3_11
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