Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/HICSS.2015.119 | System Sciences |
Keywords | Field | DocType |
knowledge based systems,learning (artificial intelligence),pattern classification,text analysis,german ad-hoc announcements,financial news sentiment analysis enhancement,machine learning,manually-labeled dataset,negated sentences,negation scope detection,positive tone measurement,predictive performance,rule-based algorithms,sentence classification,sentiment analysis accuracy improvement,source material polarity extraction,stock market returns,two-sided evaluation,speech,sentiment analysis,hidden markov models,prediction algorithms,algorithm design and analysis,computational modeling | Negation,Financial news,Sentiment analysis,Computer science,Prediction algorithms,Natural language processing,Artificial intelligence,Hidden Markov model,Stock market,German | Conference |
ISSN | Citations | PageRank |
1530-1605 | 9 | 0.61 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolas Prollochs | 1 | 27 | 7.01 |
| Stefan Feuerriegel | 2 | 219 | 31.91 |
| Dirk Neumann | 3 | 294 | 37.29 |