The Smart Performance Enhancement of Utterance Intensity Sentimental Optimization for Social Data Harvesting in Big Data Management

Abstract

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.