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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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樓主: Coagulant
21#
發(fā)表于 2025-3-25 06:39:02 | 只看該作者
Sarcasm Annotation and Detection in Tweetse-of-the-art system for automatic sarcasm detection in tweets was implemented. Experiments on the two manually annotated datasets show comparable results, while deviating considerably from results on automatically annotated data, indicating that using hashtags is not a reliable approach to creating Twitter sarcasm corpora.
22#
發(fā)表于 2025-3-25 09:54:51 | 只看該作者
23#
發(fā)表于 2025-3-25 15:18:59 | 只看該作者
Benchmarking Multimodal Sentiment Analysist modalities, and generalizability. The framework illustrates the different facets of analysis to be considered while performing multimodal sentiment analysis and, hence, serves as a new benchmark for future research in this emerging field.
24#
發(fā)表于 2025-3-25 16:43:00 | 只看該作者
25#
發(fā)表于 2025-3-25 23:49:43 | 只看該作者
26#
發(fā)表于 2025-3-26 00:26:49 | 只看該作者
Grundgleichungen der Str?mungsmechanikaffected. A formal model is specified that induces in a compositional, bottom-up manner informative relation tuples which indicate perspectives on attitudes. This enables the reader to focus on interesting cases, since they are directly accessible from the parts of the relation tuple.
27#
發(fā)表于 2025-3-26 07:29:55 | 只看該作者
28#
發(fā)表于 2025-3-26 11:57:19 | 只看該作者
CSenticNet: A Concept-Level Resource for Sentiment Analysis in Chinese Languagepaper, we present a method for the construction of a Chinese sentiment resource. We utilize both English sentiment resources and the Chinese knowledge base NTU Multi-lingual Corpus. In particular, we first propose a resource based on SentiWordNet and a second version based on SenticNet.
29#
發(fā)表于 2025-3-26 15:22:32 | 只看該作者
Verb-Mediated Composition of Attitude Relations Comprising Reader and Writer Perspectiveaffected. A formal model is specified that induces in a compositional, bottom-up manner informative relation tuples which indicate perspectives on attitudes. This enables the reader to focus on interesting cases, since they are directly accessible from the parts of the relation tuple.
30#
發(fā)表于 2025-3-26 19:16:09 | 只看該作者
Customer Churn Prediction Using Sentiment Analysis and Text Classification of VOCptures a view of customer’s attitude and feedbacks. To the best of our knowledge, this is the first work that introduces text classification of VOC to churn prediction task. Experiments show that adding VOC analysis into a conventional churn prediction model results in a significant increase in predictive performance.
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