Sentiment Analysis Techniques in Recent Works Using GRA Methodology

IF 2.8 4区 生物学 3 Biotech Pub Date : 2023-06-02 DOI:10.46632/jbab/1/3/8
Moolpani Deepak Inder
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Abstract

: A text is negative, positive, or neutral to identify that, the sentiment analysis engine Learning and Natural Language Processing uses. Two main approaches rule-based and automated Sentiment analysis. Logistic regression is a good model because it is even on large datasets Trains quickly and is very strong and provides results. Other good model choices include SVMs, Includes random forests, and naive bays. Sentiment analysis is sentiment mining Also referred to as, it is natural language (NLP) approach to processing, which is a Identify the emotional tone behind the text shows. About a product, service, or idea to determine and categorize concepts This is a popular way for companies. Feeling Analysis is performed Words as positive, negative, or neutral Text analysis and natural language for classification Using methods that use processing. It's about companies branding their customers an overview of how they feel allows for getting. Sentiment analysis is an analytical technique which to determine the emotional meaning of communication Statistics, Natural Language Processing, and Machine Learning and uses learning. Company's Customer messages, call center contacts, online Reviews, social media posts, and other content they use sentiment analysis to evaluate. Repustate's sentiment analysis software can discover the sense of slang and emoji’s, and is the sentiment behind the message negative or Determine if positive. Restate your Try out the tool to see if it suits your needs offers a free trial. Sentiment analysis technique in GRA (Gray-related analysis) method Alternative: Accuracy, Precision, and Recall. Evaluation Preference: Random forest (RF), Support vector machine (SVM), K-nearest neighbor (KNN), Naïve Bayesian (NB). from the result it is seen that Random Forest (RF) and is got the first rank whereas is the K-nearest neighbor (KNN) got is having the lowest rank. The value of the dataset for Sentiment analysis technique in GRA (Gray-related analysis) method shows that it results in Random forest (RF) and top ranking.
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近期作品中使用GRA方法的情感分析技术
:情感分析引擎学习和自然语言处理使用文本是消极的、积极的或中立的来识别。两种主要方法基于规则和自动情绪分析。逻辑回归是一个很好的模型,因为它即使在大型数据集上也能快速训练,并且非常强大并提供结果。其他好的模型选择包括svm、Includes随机森林和naive bay。情感分析也就是情感挖掘,它是一种自然语言(NLP)的处理方法,它是一种识别文本背后的情感基调的表现。关于产品、服务或想法来确定和分类概念这是公司常用的方法。情感分析是指使用处理方法对单词进行积极、消极或中性的文本分析和自然语言进行分类。这是关于公司给他们的客户打一个品牌,让他们了解他们的感受。情感分析是一种分析技术,用于确定通信统计,自然语言处理和机器学习的情感含义,并利用学习。公司的客户信息、呼叫中心联系人、在线评论、社交媒体帖子以及他们使用情感分析来评估的其他内容。Repustate的情感分析软件可以发现俚语和表情符号的含义,判断信息背后的情绪是消极的还是积极的。重申你的试用工具,看看它是否适合你的需要提供免费试用。GRA(灰色关联分析)方法中的情感分析技术可选:准确度、精密度和召回率。评价偏好:随机森林(RF)、支持向量机(SVM)、k近邻(KNN)、Naïve贝叶斯(NB)。从结果中可以看出,随机森林(RF)和随机森林是排名第一的,而随机森林得到的k近邻(KNN)是排名最低的。该数据集在GRA(灰色关联分析)方法中的情感分析技术的价值表明,它产生随机森林(RF)和顶级排名。
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来源期刊
3 Biotech
3 Biotech BIOTECHNOLOGY & APPLIED MICROBIOLOGY-
自引率
0.00%
发文量
314
期刊介绍: 3 Biotech publishes the results of the latest research related to the study and application of biotechnology to: - Medicine and Biomedical Sciences - Agriculture - The Environment The focus on these three technology sectors recognizes that complete Biotechnology applications often require a combination of techniques. 3 Biotech not only presents the latest developments in biotechnology but also addresses the problems and benefits of integrating a variety of techniques for a particular application. 3 Biotech will appeal to scientists and engineers in both academia and industry focused on the safe and efficient application of Biotechnology to Medicine, Agriculture and the Environment.
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