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Using Data Mining in the Sentiment Analysis Process on the Financial Market

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  • Cristescu Marian Pompiliu
  • Nerişanu Raluca Andreea
  • Mara Dumitru Alexandru

    (Lucian Blaga University of Sibiu, Romania)

Abstract

Sentiment analysis refers to the analysis of human opinions and sentiments that are expressed in written text, being also a part of the Natural Language Processing (NLP) tasks. Sentiment analysis can be applied in different domains, especially in the corporate marketing and sales, the healthcare system or the financial market analysis. In this paper we aim to highlight how data mining is able to extract the sentiment score from a financial platform that shows the major headlines regarding stocks, in order to highlight the publications’ positive or negative opinion over a stock. In order to gain the sentiment score we have scraped text data from the platform Finviz from which the polarity of the opinion may be extracted. We have also used Valence Aware Dictionary for Sentiment Reasoning (VADER), by running a Python script using the BeautifulSoup library. After that we have used Pandas (Python Data Analysis Library) to analyse and obtain a sentiment score on the article headlines. Results show that the script is able to generate the sentiment score for various selected stocks, while also showing graphical diagrams for the past and future trend of the stock, in terms of overall opinion on the stock performance.

Suggested Citation

  • Cristescu Marian Pompiliu & Nerişanu Raluca Andreea & Mara Dumitru Alexandru, 2022. "Using Data Mining in the Sentiment Analysis Process on the Financial Market," Journal of Social and Economic Statistics, Sciendo, vol. 11(1-2), pages 36-58, December.
    • Handle: RePEc:vrs:jsesro:v:11:y:2022:i:1-2:p:36-58:n:5
    DOI: 10.2478/jses-2022-0003
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    References listed on IDEAS

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    1. Siganos, Antonios & Vagenas-Nanos, Evangelos & Verwijmeren, Patrick, 2017. "Divergence of sentiment and stock market trading," Journal of Banking & Finance, Elsevier, vol. 78(C), pages 130-141.
    2. Deniz Can Yıldırım & Ismail Hakkı Toroslu & Ugo Fiore, 2021. "Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-36, December.
    3. Wei, Yu-Chen & Lu, Yang-Cheng & Chen, Jen-Nan & Hsu, Yen-Ju, 2017. "Informativeness of the market news sentiment in the Taiwan stock market," The North American Journal of Economics and Finance, Elsevier, vol. 39(C), pages 158-181.
    4. Traianos-Ioannis Theodorou & Alexandros Zamichos & Michalis Skoumperdis & Anna Kougioumtzidou & Kalliopi Tsolaki & Dimitris Papadopoulos & Thanasis Patsios & George Papanikolaou & Athanasios Konstanti, 2021. "An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements," Future Internet, MDPI, vol. 13(6), pages 1-22, May.
    5. Prabowo, Rudy & Thelwall, Mike, 2009. "Sentiment analysis: A combined approach," Journal of Informetrics, Elsevier, vol. 3(2), pages 143-157.
    6. Xi Zhang & Yunjia Zhang & Senzhang Wang & Yuntao Yao & Binxing Fang & Philip S. Yu, 2018. "Improving Stock Market Prediction via Heterogeneous Information Fusion," Papers 1801.00588, arXiv.org.
    7. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    8. Jianqing Fan & Juan Gu, 2003. "Semiparametric estimation of Value at Risk," Econometrics Journal, Royal Economic Society, vol. 6(2), pages 261-290, December.
    9. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
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