基于机器学习技术的详细情感分析调查

Neha Singh, U. C. Jaiswal
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引用次数: 0

摘要

由于数字信息的巨大增长,情感分析成为一个迅速发展的研究课题。在现代人工智能时代,从海量数据中获取情感数据的最关键技术之一就是情感分析。它是指对源文本中表达的观点进行查找和分类的程序。通过对消费者数据进行情感分析,可以更容易地就商业决策达成共识。机器学习为情感分类和意见挖掘提供了一种高效且值得信赖的技术。先进的机器学习技术和方法不断发展和扩展。除了总结基于电影评论、产品评论和 Twitter 评论的研究文章外,本调查文章还涉及情感分析的符号、需求、层次、方法、来源以及机器学习方法和工具。这项研究旨在确定情感分析的意义,并引起人们对这一主题的兴趣。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Detailed Sentiment Analysis Survey Based on Machine Learning Techniques
Sentiment analysis is a rapidly growing topic of research as a result of the tremendous growth of digital information. In the modern era of artificial intelligence, one of the most crucial technologies for obtaining sentiment data from the vast amounts of data is sentiment analysis. It refers to a procedure of finding and categorising the opinions expressed in a source text. Reaching a consensus regarding business decisions is made much easier by conducting a sentiment analysis on consumer data. Machine learning offers an efficient and trustworthy technique for sentiment categorization and opinion mining. State-of-art machine learning techniques and methodologies have evolved and expanded. In addition to summarising research articles based on movie reviews, product reviews, and Twitter reviews, this survey article covers sentiment analysis notations, needs, levels, methodologies, sources, and machine learning approaches and tools. This research aims to determine the significance of sentiment analysis and to generate interest in the subject.
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