NDLI: Evaluating Feature Sets and Classifiers for Sentiment Analysis of Financial News

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Evaluating Feature Sets and Classifiers for Sentiment Analysis of Financial News

Content Provider	ACM Digital Library
Author	Gulla, Jon Atle Njølstad, Pål Christian S. Høysæter, Lars S. Wei, Wei
Abstract	Work on sentiment analysis has thus far been limited in the news article domain. This has mainly been caused by 1) news articles lacking a clearly defined target, 2) the difficulty in separating good and bad news from positive and negative sentiment, and 3) the seeming necessity of, and complexity in, relying on domain-specific interpretations and background knowledge. In this paper we propose, define, experiment with, and evaluate, four different feature categories, composed of 26 article features, for sentiment analysis. Using five different machine learning methods, we train sentiment classifiers of Norwegian financial internet news articles, and achieve classification precisions up to ~71%. This is comparable to the state-of-the-art in other domains and close to the human baseline. Our experimentation with different feature subsets shows that the category relying on domain-specific sentiment lexical ('contextual' category), able to grasp the jargon and lingo used in Norwegian financial news, is of cardinal importance in classification - these features yield a precision increase of ~21% when added to the other feature categories. When comparing different machine learning classifiers, we find J48 classification trees to yield the highest performance, closely followed by Random Forests (RF), in line with recent studies, and in opposition to the antedated conception that Support Vector Machines (SVM) is superior in this domain.
Starting Page	71
Ending Page	78
Page Count	8
File Format	PDF
ISBN	9781479941438
DOI	10.1109/WI-IAT.2014.82
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-11
Access Restriction	Subscribed
Subject Keyword	Text analysis web mining feature extraction machine learning supervised learning support vector machines artificial neural networks decision trees
Content Type	Text
Resource Type	Article

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