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Document-level sentiment classification using hybrid machine learning approach

机译:使用混合机学习方法的文档级情绪分类

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摘要

It is a practice that users or customers intend to share their comments or reviews about any product in different social networking sites. An analyst usually processes to reviews properly to obtain any meaningful information from it. Classification of sentiments associated with reviews is one of these processing steps. The reviews framed are often made in text format. While processing the text reviews, each word of the review is considered as a feature. Thus, selection of right kind of features needs to be carried out to select the best feature from the set of all features. In this paper, the machine learning algorithm, i.e., support vector machine, is used to select the best features from the training data. These features are then given input to artificial neural network method, to process further. Different performance evaluation parameters such as precision, recall, f-measure, accuracy have been considered to evaluate the performance of the proposed approach on two different datasets, i.e., IMDb dataset and polarity dataset.
机译:这是用户或客户打算在不同社交网站中的任何产品分享他们的评论或评论的做法。分析师通常处理习惯评论以获得任何有意义的信息。与评论相关的情绪的分类是这些处理步骤之一。评论框架通常以文本格式制作。在处理文本评论时,审查的每个单词被视为一个功能。因此,需要进行选择正确的特征,以便从所有功能集中选择最佳特征。本文使用机器学习算法,即支持向量机,用于选择训练数据的最佳功能。然后给出这些特征对人工神经网络方法的输入,进一步处理。已经考虑了不同的性能评估参数,如精度,回忆,F测量,准确性,以评估所提出的方法的性能,即在两个不同的数据集中,即IMDB数据集和极性数据集。

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