International standard for a linguistic annotation framework
Natural Language Engineering
Using appraisal groups for sentiment analysis
Proceedings of the 14th ACM international conference on Information and knowledge management
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Annotating expressions of appraisal in English
LAW '07 Proceedings of the Linguistic Annotation Workshop
Recognition of affect, judgment, and appreciation in text
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
MAE and MAI: lightweight annotation and adjudication tools
LAW V '11 Proceedings of the 5th Linguistic Annotation Workshop
From once upon a time to happily ever after: tracking emotions in novels and fairy tales
LaTeCH '11 Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities
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Sentiment Analysis is the task of automatically identifying whether a text or a single sentence is intended to carry a positive or negative connotation. The commonly used Bag-of-Words approach that relies on counting positive and negative words, whose connotation is indicated by specially crafted sentiment dictionaries, is not ideal because it does not take into account the relations between words and how the connotation of single words changes according to the context. This paper proposes a way of identifying and analysing the targets of the opinions and their modifiers, along with their linkage (appraisal group) through an annotation schema called SentiML. Such schema has been developed in order to facilitate the identification of these elements and the annotation of their sentiment, along with advanced linguistic features such as their appraisal type according to the Appraisal Framework. The schema is XML-based and has been also designed to be language-independent. Preliminary results show that the schema allows more coverage than a sentiment dictionary, while achieving reasonably fast and reliable annotation in spite of its fine granularity.