SentiML++: An Extension of the SentiML Sentiment Annotation Scheme
In this paper, we propose SentiML++, an extension of SentiML with a focus on annotating opinions answering aspects of the general question “who has what opinion about whom in which context?”. A detailed comparison with SentiML and other existing annotatio
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Abstract. In this paper, we propose SentiML++, an extension of SentiML with a focus on annotating opinions answering aspects of the general question “who has what opinion about whom in which context?”. A detailed comparison with SentiML and other existing annotation schemes is also presented. The data collection annotated with SentiML has also been annotated with SentiML++ and is available for download for research purpose.
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Introduction
The semantic annotation of opinions is one of the very important tasks of opinion mining. Semantic annotations are very important both for training machine learning approaches and for evaluating opinion mining methods. Unfortunately, there have been hardly any serious proposal attempts of appropriate annotation schemas until recently when SentiML [2], OpinionMiningML [9] and EmotionML [10] were proposed. In this paper, we discuss, compare and identify the positives and negatives of these annotation schemes. Following this overview, we propose SentiML++, an extension of SentiML that addresses several shortcomings of the state of the art. SentiML. The SentiML annotation schema [2] follows a conventional sentiment annotation style and is based on Appraisal Framework (AF) [5] which is a strong linguistically-grounded theory. AF helps to define appraisal types (affect, judgments and appreciation) within the modifier tag which is another positive point to be noted in SentiML. With a very simple annotation scheme, SentiML is popular because adopting its annotation scheme does not require to acquire any specific skills. However, concerns can be raised about SentiML. OpinionMiningML. OpinionMiningML [9] is an XML-based formalism that allows tagging of attitude expressions for features or objects as found in a textual segment. It targets extraction of feature-based opinion expressions but its scope is limited to proposing an annotation schema. Besides this, the structure of OpinionMiningML is not straightforward and can be threatened by challenges c Springer International Publishing Switzerland 2015 F. Gandon et al. (Eds.): ESWC 2015, LNCS 9341, pp. 91–96, 2015. DOI: 10.1007/978-3-319-25639-9 18
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for feature and relation extraction while developing an automatic tagger for this annotation scheme. EmotionML. EmotionML [10] aims to make concepts from major emotion theories available in a broad range of technological contexts. Being informed by the effective sciences, EmotionML recognises the fact that there is no single agreed representation of effective states, nor of vocabularies to use. Therefore, an emotional state can be characterised using four types of descriptions: , , and . Furthermore, the vocabulary used can be identified. SentiML Example. Throughout the article, we will use the following sentence as a running example: “The U.S. State Department on Tuesday (KST) rated the human rights situation in North Korea “poor” in its annual human rights report, casting dark clouds on the already tense relationship between Pyongyang and Washington.” Relevant annotations i
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