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Vol.3 , No. 1, Publication Date: Aug. 23, 2017, Page: 1-9
| [1] | Sarkis Agaian, Department of Mathematics, New York University Courant Institute, New York, USA. |
| [2] | Petter Kolm, Department of Mathematics, New York University Courant Institute, New York, USA. |
The rise of web content has presented a great opportunity to extract indicators of investor moods directly from news and social media. Gauging this sentiment or general prevailing attitude of investors may simplify the analysis of large, unstructured textual datasets and help anticipate price developments in the market. There are several challenges in developing a scalable and effective framework for financial sentiment analysis, including: identifying useful information content, representing unstructured text in a structured format under a scalable framework, and quantifying this structured sentiment data. To address these questions, a corpus of positive and negative financial news is introduced. Various supervised machine learning algorithms are applied to gage article sentiment and empirically evaluate the performance of the proposed framework on introduced media content.
Keywords
Financial Sentiment, Sentiment Analysis, Text Categorization, Text Classification
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