面向大数据管理中社会数据采集的话语强度情感优化智能性能提升

Prabhjot Kaur
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引用次数: 0

摘要

话语强度是情感识别、情感分析和自然语言处理中的重要因素。在过去的几年里,研究人员开发了各种优化技术来提高社交数据收集的准确性,例如通过利用话语强度水平来进行情感分析。具体来说,这些技术使用各种监督和非监督技术来学习如何对话语的不同强度进行分类,以便更好地检测文本的情绪和情感。此外,他们还研究了不同的情绪强度如何影响谈话的整体情绪。研究人员希望利用话语强度优化技术来提高情感分析的准确性,从而提高社会数据收集的准确性。本文讨论了社会数据采集中理解和优化话语强度的最新技术、它们目前的局限性和潜在的未来研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Smart Performance Enhancement of Utterance Intensity Sentimental Optimization for Social Data Harvesting in Big Data Management
Utterance intensity is an important factor in emotion recognition, sentiment analysis, and natural language processing. Over the past few years, researchers have developed various optimization techniques to improve the accuracy of social data harvesting, such as sentiment analysis, by utilizing utterance intensity levels. Specifically, these techniques use various supervised and unsupervised techniques to learn how to classify different intensities of utterances in order to better detect sentiment and emotion of text. In addition, they look at how different sentiment intensities affect the overall sentiment of a conversation. By utilizing utterance intensity optimization techniques, researchers hope to improve the accuracy of sentiment analysis, thus improving the accuracy of social data harvesting. This paper discusses the state-of-the-art techniques for understanding and optimizing the utterance intensity in social data harvesting, their current limitations and potential future research directions.
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