WordNet: a lexical database for English
Communications of the ACM
A freely available morphological analyzer, disambiguator and context sensitive lemmatizer for German
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Extracting semantic orientations of words using spin model
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Proceedings of the 16th international conference on World Wide Web
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A machine learning approach to sentiment analysis in multilingual Web texts
Information Retrieval
Learning with compositional semantics as structural inference for subsentential sentiment analysis
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Parsing three German treebanks: lexicalized and unlexicalized baselines
PaGe '08 Proceedings of the Workshop on Parsing German
Review sentiment scoring via a parse-and-paraphrase paradigm
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Sentiment learning on product reviews via sentiment ontology tree
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Aspect-based sentiment analysis of movie reviews on discussion boards
Journal of Information Science
Target-dependent Twitter sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Visual sentiment summarization of movie reviews
ICADL'11 Proceedings of the 13th international conference on Asia-pacific digital libraries: for cultural heritage, knowledge dissemination, and future creation
A generic approach to generate opinion lists of phrases for opinion mining applications
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
Techniques and applications for sentiment analysis
Communications of the ACM
| Hi-index | 0.00 |
In this paper, we present a study of aspect-based opinion mining using a lexicon-based approach. We use a phrase-based opinion lexicon for the German language to investigate, how good strong positive and strong negative expressions of opinions, concerning products and services in the insurance domain, can be detected. We perform experiments on hand-tagged statements expressing opinions retrieved from the Ciao platform. The initial corpus contained about 14,000 sentences from 1,600 reviews. For both, positive and negative statements, more than 100 sentences were tagged. We show, that the algorithm can reach an accuracy of 62.2% for positive, but only 14.8% for negative utterances of opinions. We examine the cases, in which the opinion could not correctly be detected or in which the linking between the opinion statement and the aspect fails. Especially, the large gap in accuracy between positive and negative utterances is analysed.