Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
A temporal constraint structure for extracting temporal information from clinical narrative
Journal of Biomedical Informatics
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Extracting semantic orientations of words using spin model
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Deeper sentiment analysis using machine translation technology
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Determining Modality and Factuality for Text Entailment
ICSC '07 Proceedings of the International Conference on Semantic Computing
Joint extraction of entities and relations for opinion recognition
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Statement map: assisting information crediblity analysis by visualizing arguments
Proceedings of the 3rd workshop on Information credibility on the web
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Detecting experiences from weblogs
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Mining personal experiences and opinions from Web documents
Web Intelligence and Agent Systems
Extracting hidden information based on comparing web with UGC
WISS'10 Proceedings of the 2010 international conference on Web information systems engineering
Learning opinions in user-generated web content
Natural Language Engineering
Solution mining for specific contextualised problems: towards an approach for experience mining
Proceedings of the 21st international conference companion on World Wide Web
Extracting tip information from social media
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
Spatio-Temporal Web Sensors by Social Network Analysis
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Automatic organization of human task goals for web-scale problem solving knowledge
Proceedings of the seventh international conference on Knowledge capture
ACM Transactions on Asian Language Information Processing (TALIP)
Toward advice mining: conditional random fields for extracting advice-revealing text units
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Two Phase Extraction Method for Multi-label Classification of Real Life Tweets
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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This paper proposes a new UGC-oriented language technology application, which we call experience mining. Experience mining aims at automatically collecting instances of personal experiences as well as opinions from an explosive number of user generated contents (UGCs) such as weblog and forum posts and storing them in an experience database with semantically rich indices. After arguing the technical issues of this new task, we focus on the central problem, factuality analysis, among others and propose a machine learning-based solution as well as the task definition itself. Our empirical evaluation indicates that our factuality analysis task is sufficiently well-defined to achieve a high inter-annotator agreement and our Factorial CRF-based model considerably outperforms the baseline. We also present an application system, which currently stores over 30M experience instances extracted from 150M Japanese blog posts with semantic indices and is scheduled to start serving as an experience search engine for unrestricted users in October.