Machine Learning
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 product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Sentiment summarization: evaluating and learning user preferences
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Aspect-based sentence segmentation for sentiment summarization
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Sentiment analysis with a multilingual pipeline
WISE'11 Proceedings of the 12th international conference on Web information system engineering
IEEE Internet Computing
Hi-index | 0.00 |
The phenomenon of big data makes managing, processing, and extracting valuable information from the Web an increasingly challenging task. As such, the abundance of user-generated content with opinions about products or brands requires appropriate tools in order to be able to capture consumer sentiment. Such tools can be used to aggregate content by means of sentiment summarization techniques, extracting text segments that reflect the overall sentiment of a text in a compressed form. We explore what features distinguish relevant from irrelevant text segments in terms of the extent to which they reflect the overall sentiment of conversational documents. In our empirical study on a collection of Dutch conversational documents, we find that text segments with opinions, segments with arguments supporting these opinions, segments discussing aspects of the subject of a text, and relatively long sentences are key indicators for text segments that summarize the sentiment conveyed by a text as a whole.