DocumentCode :
3334923
Title :
Using SentiWordNet for multilingual sentiment analysis
Author :
Denecke, Kerstin
Author_Institution :
Res. CenterL3S, Hannover
fYear :
2008
fDate :
7-12 April 2008
Firstpage :
507
Lastpage :
512
Abstract :
This paper introduces a methodology for determining polarity of text within a multilingual framework. The method leverages on lexical resources for sentiment analysis available in English (SentiWordNet). First, a document in a different language than English is translated into English using standard translation software. Then, the translated document is classified according to its sentiment into one of the classes "positive" and "negative". For sentiment classification, a document is searched for sentiment bearing words like adjectives. By means of SentiWordNet, scores for positivity and negativity are determined for these words. An interpretation of the scores then leads to the document polarity. The method is tested for German movie reviews selected from Amazon and is compared to a statistical polarity classifier based on n-grams. The results show that working with standard technology and existing sentiment analysis approaches is a viable approach to sentiment analysis within a multilingual framework.
Keywords :
language translation; natural language processing; word processing; Amazon; English; German movie; SentiWordNet; document polarity; lexical resources; multilingual sentiment analysis; n-grams; sentiment classification; standard translation software; statistical polarity classifier; text polarity; Data mining; Humans; Information services; Machine learning algorithms; Motion pictures; Natural language processing; Natural languages; Software standards; Testing; Web sites;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Engineering Workshop, 2008. ICDEW 2008. IEEE 24th International Conference on
Conference_Location :
Cancun
Print_ISBN :
978-1-4244-2161-9
Electronic_ISBN :
978-1-4244-2162-6
Type :
conf
DOI :
10.1109/ICDEW.2008.4498370
Filename :
4498370
Link To Document :
بازگشت