COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Automatic acquisition of a large subcategorization dictionary from corpora
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Identifying sources of opinions with conditional random fields and extraction patterns
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Opinion holder extraction from author and authority viewpoints
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Identifying Opinion Holders in Opinion Text from Online Newspapers
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Extracting opinions, opinion holders, and topics expressed in online news media text
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Analysis and tracking of emotions in english and bengali texts: a computational approach
Proceedings of the 20th international conference companion on World wide web
Roles of event actors and sentiment holders in identifying event-sentiment association
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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Human-like holder plays an important role in identifying actual emotion expressed in text. This paper presents a baseline followed by syntactic approach for capturing emotion holders in the emotional sentences. The emotional verbs collected from WordNet Affect List (WAL) have been used in extracting the holder annotated emotional sentences from VerbNet. The baseline model is developed based on the subject information of the dependency-parsed emotional sentences. The unsupervised syntax based model is based on the relationship of the emotional verbs with their argument structure extracted from the head information of the chunks in the parsed sentences. Comparing the system extracted argument structure with available VerbNet frames' syntax for 942 emotional verbs, it has been observed that the model based on syntax outperforms the baseline model. The precision, recall and F-Score values for the baseline model are 63.21%, 66.54% and 64.83% and for the syntax based model are 68.11%, 65.89% and 66.98% respectively on a collection of 4,112 emotional sentences.