Ontological Reasoning to Configure Emotional Voice Synthesis

Francisco, Virginia; Gervás, Pablo; Peinado, Federico Lara

doi:10.1007/978-3-540-72982-2_7

Cited by 21 publications

(19 citation statements)

References 4 publications

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“…lexicons and libraries) that are already available. The tool that we developed, ArsEmotica, succeeded in populating an ontology of emotions (based on [11]) with Italian emotion-bearing terms. Moreover, the first experiments proved that it is capable of extracting the prevalent emotions that are hidden inside the tags, associated to a digitalized visual artwork, a kind of response that is much more informative than the general polarized appreciation, returned by standard sentiment analysis tools.…”

Section: Discussionmentioning

confidence: 99%

“…Our starting point was OntoEmotion, an emotional ontology developed at Universidad Complutense de Madrid [11], that met our requirement to have a taxonomic structure, mirroring well-founded psychological models of emotions, and that was implemented by using semantic web technologies. The ontology is written in OWL and structures emotional categories in a taxonomy, which includes 87 emotional concepts.…”

Section: The Ontology Of Emotions and The Italian Emotional Wordsmentioning

confidence: 99%

“…Hence the development of Opinion Mining and Sentiment Analysis [17,7], of ontologies of emotions, like the one we started out work from [11], and of W3C markup language proposals [2]. However, there are still few applications that use the most advanced results in Semantic Web technology to deal with emotions [14] and most of the approaches use ontologies where emotions are individual, isolated units (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…WordAffect). Such considerations motivated our focus on the ontology of emotions in [11], an OWL ontology where emotions are structured and organized in levels, trying to integrate the results of the most recent psychological models. OntoEmotion provided a good starting point to explore an ontology-driven approach to Sentiment Analysis, where tags (and then tagged resources) are related to emotions.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Sentiment Analysis in the Planet Art: A Case Study in the Social Semantic Web

Baldoni

Baroglio

Patti

et al. 2013

New Challenges in Distributed Information Filtering and Retrieval

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Affective computing is receiving increasing attention in many sectors, ranging from advertisement to politics. Its application to the Planet Art, however, is quite at its beginning, especially for what concerns the visual arts. This work, set in a Social Semantic Web framework, explores the possibility of extracting rich, emotional semantic information from the tags freely associated to digitalized visual artworks, identifying the prevalent emotions that are captured by the tags. This is done by means of ArsEmotica, an application software that we developed and that combines an ontology of emotional concepts with available computational and sentiment lexicons. Besides having made possible the enrichment of the ontology with over four-hundred Italian terms, ArsEmotica is able to analyse the emotional semantics of a tagged artwork by working at different levels: not only it can compute a semantic value, captured by tags that can be directly associated to emotional concepts, but it can also compute the semantic value of tags that can be ascribed to emotional concepts only indirectly. The results of a user study, aimed at validating the outcomes of ArsEmotica, are reported and commented. They were obtained by involving the users of the same community which tagged the artworks. It is important to observe that the tagging activity was not performed with the aim of later applying some kind of Sentiment Analysis, but in a pure Web 2.0 approach, i.e. as a form of spontaneous annotation produced by the members of the community on one another's artworks.

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Section: Discussionmentioning

confidence: 99%

Section: The Ontology Of Emotions and The Italian Emotional Wordsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sentiment Analysis in the Planet Art: A Case Study in the Social Semantic Web

Baldoni

Baroglio

Patti

et al. 2013

New Challenges in Distributed Information Filtering and Retrieval

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“…We have developed DEMLOnto ontology ascended from [8,9] with some changes. Our ontology is designed on six basic emotions: love, joy, anger, sad, fear, surprise.…”

Section: A How To Deal With Emotionsmentioning

confidence: 99%

Building Ontology for Different Emotional Contexts and Multilingual Environment in Opinion Mining

Wan

Liu

2016

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-With the explosive growth of various social media applications, individuals and organizations are increasingly using their contents (e.g., reviews, forum discussions, blogs, microblogs, comments, and postings in social network sites) for decision making. These contents are typical big data. Opinion mining or sentiment analysis focuses on how to extract emotional semantics from these big data to help users to get a better decision. That is not an easy task because it faces many problems, such as different context may make the meaning of the same word change variously, at the same time multilingual environment restricts the full use of the analysis results. Ontology provides knowledge about specific domains that are understandable by both the computers and developers. Building ontology is mainly a useful first step in providing and formalizing the semantics of information representation. We proposed an ontology DEMLOnto based on six basic emotions to help users to share existed information. The ontology DEMLOnto would help in identifying the opinion features associated with the contextual environment, which may change along with applications. We built the ontology according to ontology engineering. It was developed on the platform Proté gé by using OWL.

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Moods in Book Reviews: Text Mining Approach

Cho

Adkins

Bossaller

et al. 2021

Proceedings of the Association for Information Science and Tech

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Spiteri and Pecoskie (2018) proposed a taxonomy of terms to describe emotion and tone in novels. We tested those terms against 5,144 full-text book reviews from the New York Times Book Review to discover whether the proposed terms were used in published reviews to describe books, and of those terms used, which were most used. The objective of this study is to explore whether emotional contents can be identified by a text mining approach without investing too much time and efforts of information professionals. Findings demonstrate that the terms chosen by Spiteri and Pecoskie are used in professional book reviews, though some may be used in multiple ways, rather than only related to emotional content. Results of this work contribute to a larger scale project of testing machine models of identifying emotional content in books and ultimately being able to create automated media recommendation systems that include emotion as an identifier.

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Ontological Reasoning to Configure Emotional Voice Synthesis

Cited by 21 publications

References 4 publications

Sentiment Analysis in the Planet Art: A Case Study in the Social Semantic Web

Sentiment Analysis in the Planet Art: A Case Study in the Social Semantic Web

Building Ontology for Different Emotional Contexts and Multilingual Environment in Opinion Mining

Moods in Book Reviews: Text Mining Approach

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