NDLI: Learning the Roles of Directional Expressions and Domain Concepts in Financial News Analysis

Content Provider	IEEE Xplore Digital Library
Author	Malo, P. Sinha, A. Takala, P. Ahlgren, O. Lappalainen, I.
Copyright Year	2013
Description	Author affiliation: Sch. of Bus., Dept. of Inf. & Service Econ., Aalto Univ., Aalto, Finland (Malo, P.; Sinha, A.; Takala, P.; Ahlgren, O.; Lappalainen, I.)
Abstract	Rapid development of natural language processing technologies has paved way for automatic sentiment analysis and emergence of robo-readers in computational finance. However, the technology is still in its nascent state. Distilling sentiment information from unstructured sources has turned out to be a complicated and strongly domain-dependent problem. To emulate the human ability to recognize financial sentiments in natural language by using machines, we need to provide them with (i) necessary ontological knowledge on the relevant domain-concepts, and (ii) learning strategies that help the machines to combine this knowledge with the syntactic structures extracted from text. In this paper, we present a knowledge-driven tree kernel framework for sentence-level analysis of financial news sentiments. Comparisons with linear kernels and classical lexicon-based systems suggest that significant performance gains can be achieved by incorporating information on financial concepts and their grammatical context. The framework is decomposable into learning, knowledge and syntactic structure components. Contribution of each part is separately examined using a human-annotated phrase-bank with close to 5000 sentences collected across a number of financial news sources. The proposed sentiment analysis framework is flexible and can be applied also outside financial domain. To evaluate cross-domain performance, a further comparison of the algorithms is done with datasets from non-financial domains including movie reviews and general political discussions.
Sponsorship	Toshiba
Starting Page	945
Ending Page	954
File Size	1279511
Page Count	10
File Format	PDF
e-ISBN	9781479931422
DOI	10.1109/ICDMW.2013.36
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Economics Databases support vector machines Semantics sentiment analysis tree kernels Syntactics computational finance Data mining Kernel polarity
Content Type	Text
Resource Type	Article

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