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CDNB: CAVIAR-Dragonfly Optimization with Naive Bayes for the Sentiment and Affect Analysis in Social Media.
Big Data ( IF 2.6 ) Pub Date : 2020-04-17 , DOI: 10.1089/big.2019.0130
Harshali P Patil 1 , Mohammad Atique 2
Affiliation  

With the advent of the new information technologies, the growth of online reviews regarding an organization or a company or any other sector has been playing a vital role in improving the sector plans and decisions. The vast significance of the online reviews that determine the sentiment polarity is the hectic challenge of the current scenario. Sentiment classification is a process of classifying the text according to the sentimental polarities of opinions, which has positive or negative. Thus, this article concentrates on presenting a novel method, named CAVIAR-Dragonfly optimization with Extended Naive Bayes (CDNB), for performing sentiment classification and affective state classification. At first, the BITS review from Twitter is subjected to preprocessing, which includes stop word removal and stemming. Then, the next step is the feature extraction, in which all the reviews are converted to a feature vector. After that, all the individual feature vectors are collected to form the feature matrix, which is applied to the proposed C-Dragonfly optimization algorithm, to perform the sentiment classification and affective state classification. The performance of the proposed method is analyzed using the Twitter Sentiment Analysis Training Corpus Data Set based on true positive rate (TPR), true negative rate (TNR), and accuracy. From the analysis, it can be shown that the proposed method yields the maximum TPR, TNR, and accuracy of 89.0934%, 72.3064%, and 79.3591% for sentiment classification and 84.2122%, 66.2187%, and 76.6249% for the sentiment affective state classification.

中文翻译:

CDNB:使用天真贝叶斯进行CAVIAR-蜻蜓优化,用于社交媒体中的情感和情感分析。

随着新信息技术的出现,有关组织或公司或任何其他部门的在线评论的增长在改善部门计划和决策中起着至关重要的作用。决定情感极性的在线评论的巨大意义是当前情况的激烈挑战。情感分类是根据情感的正面或负面观点对文本进行分类的过程。因此,本文着重介绍一种新颖的方法,该方法称为CAVIAR-蜻蜓优化和扩展朴素贝叶斯(CDNB),用于执行情感分类和情感状态分类。首先,来自Twitter的BITS审查需要经过预处理,包括停用词的删除和词干。然后,下一步是特征提取,其中所有评论都转换为特征向量。之后,收集所有单个特征向量以形成特征矩阵,将其应用于提出的C-Dragonfly优化算法,以进行情感分类和情感状态分类。使用Twitter情绪分析训练语料库数据集,基于真实阳性率(TPR),真实阴性率(TNR)和准确性对提出的方法的性能进行了分析。从分析中可以看出,所提出的方法对情感分类的最大TPR,TNR和准确度分别为89.0934%,72.3064%和79.3591%,对于情感情感状态分类的准确率则为84.2122%,66.2187%和76.6249% 。收集所有单个特征向量以形成特征矩阵,将其应用于提出的C-Dragonfly优化算法,以进行情感分类和情感状态分类。使用Twitter情绪分析训练语料库数据集,基于真实阳性率(TPR),真实阴性率(TNR)和准确性对提出的方法的性能进行了分析。从分析中可以看出,所提出的方法对情感分类的最大TPR,TNR和准确度分别为89.0934%,72.3064%和79.3591%,对于情感情感状态分类的准确率则为84.2122%,66.2187%和76.6249% 。收集所有单个特征向量以形成特征矩阵,将其应用于提出的C-Dragonfly优化算法,以进行情感分类和情感状态分类。使用Twitter情绪分析训练语料库数据集,基于真实阳性率(TPR),真实阴性率(TNR)和准确性对提出的方法的性能进行了分析。从分析中可以看出,所提出的方法对情感分类的最大TPR,TNR和准确度分别为89.0934%,72.3064%和79.3591%,对于情感情感状态分类的准确率则为84.2122%,66.2187%和76.6249% 。执行情感分类和情感状态分类。使用Twitter情绪分析训练语料库数据集,基于真实阳性率(TPR),真实阴性率(TNR)和准确性对提出的方法的性能进行了分析。从分析中可以看出,所提出的方法对情感分类的最大TPR,TNR和准确度分别为89.0934%,72.3064%和79.3591%,对于情感情感状态分类的准确率则为84.2122%,66.2187%和76.6249% 。执行情感分类和情感状态分类。使用Twitter情绪分析训练语料库数据集,基于真实阳性率(TPR),真实阴性率(TNR)和准确性对提出的方法的性能进行了分析。从分析中可以看出,所提出的方法对情感分类的最大TPR,TNR和准确度分别为89.0934%,72.3064%和79.3591%,对于情感情感状态分类的准确率则为84.2122%,66.2187%和76.6249% 。
更新日期:2020-04-17
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