Making large-scale support vector machine learning practical
Advances in kernel methods
An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Combining the language model and inference network approaches to retrieval
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
Mining knowledge from text using information extraction
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
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
OpinionFinder: a system for subjectivity analysis
HLT-Demo '05 Proceedings of HLT/EMNLP on Interactive Demonstrations
Scalability of the Nutch search engine
Proceedings of the 21st annual international conference on Supercomputing
Annotating attributions and private states
CorpusAnno '05 Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky
Joint extraction of entities and relations for opinion recognition
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
SemEval-2007 task 04: classification of semantic relations between nominals
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Extracting opinions, opinion holders, and topics expressed in online news media text
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Recognizing stances in online debates
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
LaTeCH '11 Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities
Grammatical structures for word-level sentiment detection
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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We present a technique for identifying the sources and targets of opinions without actually identifying the opinions themselves. We are able to use an information extraction approach that treats opinion mining as relation mining; we identify instances of a binary "expresses-an-opinion-about" relation. We find that we can classify source-target pairs as belonging to the relation at a performance level significantly higher than two relevant baselines. This technique is particularly suited to emerging approaches in corpus-based social science which focus on aggregating interactions between sources to determine their effects on socio-economically significant targets. Our application is the analysis of information technology (IT) innovations. This is an example of a more general problem where opinion is expressed using either sub- or supersets of expressive words found in newswire. We present an annotation scheme and an SVM-based technique that uses the local context as well as the corpus-wide frequency of a source-target pair as data to determine membership in "expresses-an-opinion-about". While the presence of conventional subjectivity keywords appears significant in the success of this technique, we are able to find the most domain-relevant keywords without sacrificing recall.