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Synopsis
In a world progressively digitalised, it is arguable whether Humanity will one day need to be re-defined. If chemical processes, such as emotions, are one day understood by “digital beings”, Humanity would probably enter the “Transisto-Sapiens” era. In this project, the focus is made on identifying feelings in texts. Extracted from Twitter, a social media allowing its community to answer the question: “what are you doing?” in 140 characters, these texts usually display a lack of grammatical structure. The classical approach considering tools such as a sentence parser or a POS tagger does not apply. Indeed, as low informational content is available, a too strict feature reduction policy would often result in no text at all. The interest is thus to evaluate the accuracy one can expect on a corpus not pre-processed at all. Focusing solely on surface features, a metric measuring the emotional content of a particular concept is required. To the best of the author’s knowledge, none has been done so far. Using WordNet combined with the Plutchik affective model, a simple edge-based metric has thus been designed.
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About the Author
Thomas Belin attended Heriot-Watt University, in Scotland, and graduated in 2009 a MSc in Intelligent Web Technology. He is the co-founder of gem4web SAS and currently involved as a devops engineer, in a major actor within the telecommunication industry. He is also the happy dad of a 5-month-old baby girl, Maya, to whom this book is dedicated.
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- PublisherLAP LAMBERT Academic Publishing
- Publication date2017
- ISBN 10 3659971324
- ISBN 13 9783659971327
- BindingPaperback
- LanguageEnglish
- Number of pages132
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Learning emotions using automated affect sensing techniques
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LAP LAMBERT Academic Publishing Jan 2017, 2017
ISBN 10: 3659971324
ISBN 13: 9783659971327
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In a world progressively digitalised, it is arguable whether Humanity will one day need to be re-defined. If chemical processes, such as emotions, are one day understood by 'digital beings', Humanity would probably enter the 'Transisto-Sapiens' era. In this project, the focus is made on identifying feelings in texts. Extracted from Twitter, a social media allowing its community to answer the question: 'what are you doing ' in 140 characters, these texts usually display a lack of grammatical structure. The classical approach considering tools such as a sentence parser or a POS tagger does not apply. Indeed, as low informational content is available, a too strict feature reduction policy would often result in no text at all. The interest is thus to evaluate the accuracy one can expect on a corpus not pre-processed at all. Focusing solely on surface features, a metric measuring the emotional content of a particular concept is required. To the best of the author's knowledge, none has been done so far. Using WordNet combined with the Plutchik affective model, a simple edge-based metric has thus been designed. 132 pp. Englisch. Seller Inventory # 9783659971327
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Learning emotions using automated affect sensing techniques : Plecrus AI
Published by
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3659971324
ISBN 13: 9783659971327
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Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In a world progressively digitalised, it is arguable whether Humanity will one day need to be re-defined. If chemical processes, such as emotions, are one day understood by 'digital beings', Humanity would probably enter the 'Transisto-Sapiens' era. In this project, the focus is made on identifying feelings in texts. Extracted from Twitter, a social media allowing its community to answer the question: 'what are you doing ' in 140 characters, these texts usually display a lack of grammatical structure. The classical approach considering tools such as a sentence parser or a POS tagger does not apply. Indeed, as low informational content is available, a too strict feature reduction policy would often result in no text at all. The interest is thus to evaluate the accuracy one can expect on a corpus not pre-processed at all. Focusing solely on surface features, a metric measuring the emotional content of a particular concept is required. To the best of the author's knowledge, none has been done so far. Using WordNet combined with the Plutchik affective model, a simple edge-based metric has thus been designed. Seller Inventory # 9783659971327
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Learning emotions using automated affect sensing techniques: Plecrus AI
Published by
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3659971324
ISBN 13: 9783659971327
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Paperback. Condition: Brand New. 132 pages. 8.66x5.91x0.30 inches. In Stock. Seller Inventory # 3659971324
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Learning emotions using automated affect sensing techniques: Plecrus AI
Published by
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ISBN 10: 3659971324
ISBN 13: 9783659971327
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paperback
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paperback. Condition: New. New. book. Seller Inventory # D8S0-3-M-3659971324-6
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