Machine Learning for Information Extraction in Informal Domains
Machine Learning - Special issue on information retrieval
Visualizing internetworked argumentation
Visualizing argumentation
The rhetorical parsing of unrestricted texts: a surface-based approach
Computational Linguistics
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Computing Attitude and Affect in Text: Theory and Applications (The Information Retrieval Series)
Computing Attitude and Affect in Text: Theory and Applications (The Information Retrieval Series)
Anaphora and Discourse Structure
Computational Linguistics
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
Adaptive information extraction
ACM Computing Surveys (CSUR)
Memory-Based Language Processing (Studies in Natural Language Processing)
Memory-Based Language Processing (Studies in Natural Language Processing)
Comments-oriented blog summarization by sentence extraction
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Argumentation mining: the detection, classification and structure of arguments in text
Proceedings of the 12th International Conference on Artificial Intelligence and Law
Techniques for recognizing textual entailment and semantic equivalence
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
Polarity analysis of texts using discourse structure
Proceedings of the 20th ACM international conference on Information and knowledge management
Exploiting emoticons in sentiment analysis
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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The recent turmoil in the financial markets has demonstrated the growing need for automated information monitoring tools that can help to identify the issues and patterns that matter and that can track and predict emerging events in business and economic processes. One of the techniques that can address this need is sentiment mining. Existing approaches enable the analysis of a large number of text documents, mainly based on their statistical properties and possibly combined with numeric data. Most approaches are limited to simple word counts and largely ignore semantic and structural aspects of content. Yet, argumentation plays an important role in expressing and promoting an opinion. Therefore, we propose a framework that allows the incorporation of information on argumentation structure in the models for economic sentiment discovery in text.