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Titlebook: Computational Linguistics and Intelligent Text Processing; 18th International C Alexander Gelbukh Conference proceedings 2018 Springer Natu

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楼主: Coagulant
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,Grundgleichungen der Strömungsmechanik,ensification, conditionality, tense, interrogation, and modality) within a large corpus of tweets with emotional hashtags. Our study sheds light on how to model negation relations between given emotions, reveals the impact of previously under-studied modifiers, and suggests how to detect more precise emotional statements.
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BATframe: An Unsupervised Approach for Domain-Sensitive Affect Detectionhuman–annotated affect values. Our framework outperforms the state–of–the–art generic and domain–specific approaches with a precision of over 70% for the emotion detection task on the SemEval 2007 Affect Corpus.
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Mining Aspect-Specific Opinions from Online Reviews Using a Latent Embedding Structured Topic Modelpinions from small or large numbers of reviews and to assign accurate sentiment to words. Experimental results for topic coherence, document sentiment classification, and a human evaluation all show that our proposed model achieves significant improvements over several state-of-the-art baselines.
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