Learning subjective nouns using extraction pattern bootstrapping
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
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
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
A study in rule-specific issue categorization for e-rulemaking
dg.o '08 Proceedings of the 2008 international conference on Digital government research
Which side are you on?: identifying perspectives at the document and sentence levels
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
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
Web-scale distributional similarity and entity set expansion
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Cross-domain sentiment classification via spectral feature alignment
Proceedings of the 19th international conference on World wide web
Recognizing stances in ideological on-line debates
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Automatically extracting polarity-bearing topics for cross-domain sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Learning word vectors for sentiment analysis
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Target-dependent Twitter sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Contrasting opposing views of news articles on contentious issues
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Automatically evaluating text coherence using discourse relations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Aspect ranking: identifying important product aspects from online consumer reviews
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Collective classification of congressional floor-debate transcripts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Discourse-level relations for opinion analysis
Discourse-level relations for opinion analysis
Cats rule and dogs drool!: classifying stance in online debate
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
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In this paper we investigate two distinct tasks. The first task involves detecting arguing subjectivity, a type of linguistic subjectivity on which relatively little work has yet to be done. The second task involves labeling instances of arguing subjectivity with argument tags reflecting the conceptual argument being made. We refer to these two tasks collectively as "recognizing arguments". We develop a new annotation scheme and assemble a new annotated corpus to support our learning efforts. Through our machine learning experiments, we investigate the utility of a sentiment lexicon, discourse parser, and semantic similarity measures with respect to recognizing arguments. By incorporating information gained from these resources, we outperform a unigram baseline by a significant margin. In addition, we explore a two-phase approach to recognizing arguments, with promising results.