On the computation of point of view
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Affective computing
A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
Describing the emotional states that are expressed in speech
Speech Communication - Special issue on speech and emotion
Emotional speech: towards a new generation of databases
Speech Communication - Special issue on speech and emotion
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Affective computing: challenges
International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction
Subjective understanding: computer models of belief systems.
Subjective understanding: computer models of belief systems.
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Tracking point of view in narrative
Computational Linguistics
A computational theory of perspective and reference in narrative
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Development and use of a gold-standard data set for subjectivity classifications
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
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
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
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
2005 Special Issue: Challenges in real-life emotion annotation and machine learning based detection
Neural Networks - Special issue: Emotion and brain
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Emotions from text: machine learning for text-based emotion prediction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Learning to identify emotions in text
Proceedings of the 2008 ACM symposium on Applied computing
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Communications of the ACM - A Direct Path to Dependable Software
Identifying expressions of emotion in text
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Emotional sequencing and development in fairy tales
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Automated mark up of affective information in english texts
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
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Emotions are inherent to any human activity, including human---computer interactions, and that is the reason why recognizing emotions expressed in natural language is becoming a key feature for the design of more natural user interfaces. In order to obtain useful corpora for this purpose, the manual classification of texts according to their emotional content has been the technique most commonly used by the research community. The use of corpora is widespread in Natural Language Processing, and the existing corpora annotated with emotions support the development, training and evaluation of systems using this type of data. In this paper we present the development of an annotated corpus oriented to the narrative domain, called EmoTales, which uses two different approaches to represent emotional states: emotional categories and emotional dimensions. The corpus consists of a collection of 1,389 English sentences from 18 different folk tales, annotated by 36 different people. Our model of the corpus development process includes a post-processing stage performed after the annotation of the corpus, in which a reference value for each sentence was chosen by taking into account the tags assigned by annotators and some general knowledge about emotions, which is codified in an ontology. The whole process is presented in detail, and revels significant results regarding the corpus such as inter-annotator agreement, while discussing topics such as how human annotators deal with emotional content when performing their work, and presenting some ideas for the application of this corpus that may inspire the research community to develop new ways to annotate corpora using a large set of emotional tags.