Research on the Cultivation of Practical English Talents Based on a Big Data-Driven Model and Sentiment Dictionary Analysis by Qiuwei Fang

“…Notably, the precision of recognizing positive, negative, and neutral emotions reaches an impressive 92.5%, marking a notable improvement over methods devoid of dictionary feature integration. …”
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“Stepchildren of free Russia”: collective consciousness of orthodox priests during russian revolution by Gleb Zapalsky

“…These texts highlight practical, even down-to-earth problems, their rhetoric and emotions reach a grotesque level with events of the revolution in the background. …”
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Optimizing the interaction of service robots in elderly care institutions using multi-modal emotion recognition system based on transfer learning by Yongguan Ai, Yuanjun Zhang, Juan Wang, Nianfang Xu

“…In terms of emotion recognition accuracy, the experimental group's average recognition accuracy for multiple emotions reaches 84.1%, with an F1 score of 82.5%, precision of 83.2% and recall of 81.8%. …”
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