Authors:
Bernd Markscheffel
and
Markus Haberzettl
Affiliation:
Department of Information and Knowledge Management, Technische Universität Ilmenau, Ilmenau and Germany
Keyword(s):
Sentiment Analysis, Literature Analysis, Machine Learning, Feature Extraction Methods.
Related
Ontology
Subjects/Areas/Topics:
Business Analytics
;
Data Engineering
;
Data Management and Quality
;
Information Quality
;
Text Analytics
Abstract:
The increasing number of emails sent daily to the customer service of companies confronts them with new challenges. In particular, a lack of resources to deal with critical concerns, such as complaints, poses a threat to customer relations and the public perception of companies. Therefore, it is necessary to prioritize these concerns in order to avoid negative effects. Sentiment analysis, i.e. the automated recognition of the mood in texts, makes such prioritisation possible. The sentiment analysis of German-language emails is still an open research problem. Moreover, there is no evidence of a dominant approach in this context. Therefore two approaches are compared, which are applicable in the context of the problem definition mentioned. The first approach is based on the combination of sentiment lexicons and machine learning methods. This is to be extended by the second approach in such a way that in addition to the lexicons further features are used. These features are to be genera
ted by the use of feature extraction methods. The methods used in both approaches are investigated in a systematic literature search. A Gold Standard corpus is generated as basis for the comparison of these approaches. Systematic experiments are carried out in which the different method combinations for the approaches are examined. The results of the experiments show that the combination of feature extracting methods with Sentiment lexicons and machine learning approaches generates the best classification results.
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