Enhancing Sentiment Analysis of Financial News by Detecting Negation Scopes

This paper uses German ad hoc announcements as a common example of financial news in order to pursue a two-sided evaluation of rule-based and machine learning methods for sentiment analysis, and examines how detecting negation scopes can improve the accuracy of sentiment analysis.

Abstract

Sentiment analysis refers to the extraction of the polarity of source materials, such as financial news. However, measuring positive tone requires the correct classification of sentences that are negated, i.e. The negation scopes. For example, around 4.74% of all sentences in German ad hoc announcements contain negations. To predict the corresponding negation scope, related literature commonly utilizes two approaches, namely, rule-based algorithms and machine learning. Nevertheless, a thorough comparison is missing, especially for the sentiment analysis of financial news. To close this gap, this paper uses German ad hoc announcements as a common example of financial news in order to pursue a two-sided evaluation. First, we compare the predictive performance using a manually-labeled dataset. Second, we examine how detecting negation scopes can improve the accuracy of sentiment analysis. In this instance, rule-based algorithms produce superior results, resulting in an improvement of up to 9.80% in the correlation between news sentiment and stock market returns.