Enhancing Sentiment Analysis of Financial News by Detecting Negation Scopes

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, rulebased algorithms produce superior results, resulting in an improvement of up to 9.80 % in the correlation between news sentiment and stock market returns.

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“…Pröllochs et al [43] Not, *n't No, rather, hardly, never Deny Without 8 Taboada et al [21] Not Nobody, nothing, none never Lack Without 7…”

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“…In order to generate these sentiment scores, we need to prepare the text corpus of the news we want to analyze. Therefore, we start by tokenizing the text [25], detecting and inverting negation [26] and removing stop words [27]. Finally, we perform stemming for the remaining text with the Porter stemming algorithm [28] to generate our final text corpus.…”