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A Survey of Sentiment Analysis for Journal Citation

机译:期刊引文情感分析研究

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Sentiment analysis approach belongs to the family of machine learning, where the objective is to discover useful patterns stored in a database. Due to wide availability of data, there is an upcoming need for turning such an overwhelming amount of data into useful knowledge. In this paper we recommend different techniques available for high accuracy extraction of citations for academic papers and improve the performance in citation extraction by integration of two techniques. Therefore our aim is to automate the task to determine whether a context is positive or negative. The main goal of sentiment analysis lies in finding the polarity of citation expressed in different research article. In this paper we address the techniques, approaches and methods of the research which are supportive and marked as the essential field of sentiment analysis of citations in research article. This literature survey is done to study the sentiment analysis problem in journal citation to identify different trends and recommend the upcoming research directions.
机译:情感分析方法属于机器学习家族,其目的是发现存储在数据库中的有用模式。由于数据的广泛可用性,迫切需要将如此大量的数据转化为有用的知识。在本文中,我们为学术论文推荐了可用于高精度引文提取的不同技术,并通过整合两种技术来提高引文提取的性能。因此,我们的目标是使任务自动化,以确定上下文是肯定的还是否定的。情感分析的主要目标在于找到不同研究文章中表达的引文极性。在本文中,我们将探讨研究的技术,方法和方法,这些技术,方法和方法是支持性的,并且在研究文章中被标记为引用情绪分析的重要领域。进行这项文献调查是为了研究期刊引文中的情感分析问题,以发现不同的趋势并为即将到来的研究方向提供建议。

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