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Learning Models for Opinion Mining Over a Fundamental Analysis Corpus

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dc.contributor.advisor Chandlee, Jane
dc.contributor.author Cassidy, Connor
dc.date.accessioned 2017-08-30T18:12:05Z
dc.date.available 2017-08-30T18:12:05Z
dc.date.issued 2017
dc.identifier.uri http://hdl.handle.net/10066/19298
dc.description.abstract This paper presents a comprehensive survey of the methodologies and techniques used in opinion mining. Opinion mining, also known as sentiment analysis, refers to the use of natural language processing, text analysis, and computational linguistics to identify and extract subjective information from a variety of source materials. In this paper, we provide historical background and insight to the evolution of methodologies that compose the cutting edge techniques in sentiment analysis. Our goal in completing this survey is to compare the field's techniques in order to establish and propose a best fit model for the mining of opinions within a financial news corpus. Based on results gathered from this research, we establish a method to accurately [16] extract sentiment from qualitative fundamental analysis text using Recursive Neural Tensor Networks [3] (RNTNs) along with phrase-level assessments of our corpus. In future work, we aim to use this proposed model to capture sentiment from our corpus at a topic level in order to assess and estimate the dynamic feedback effect of news in markets and sectors.
dc.description.sponsorship Haverford College. Department of Computer Science
dc.language.iso eng
dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/
dc.title Learning Models for Opinion Mining Over a Fundamental Analysis Corpus
dc.type Thesis
dc.rights.access Open Access


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http://creativecommons.org/licenses/by-nc/4.0/ Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc/4.0/

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