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Classi?cation of Sentimental Reviews Using Machine Learning Techniques

机译:使用机器学习技术对情感评论进行分类

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Sentiment Analysis is the most prominent branch of natural language processing. It deals with the text classi?cation in order to determine the intention of the author of the text. The intention can be of admiration (positive) or criticism (Negative) type. This paper presents a comparison of results obtained by applying Naive Bayes (NB) and Support Vector Machine (SVM) classi?cation algorithm. These algorithms are used to classify a sentimental review having either a positive review or negative review. The dataset considered for training and testing of model in this work is labeled based on polarity movie dataset and a comparison with results available in existing literature has been made for critical examination.
机译:情感分析是自然语言处理的最突出分支。它处理文本分类,以确定文本作者的意图。目的可以是钦佩(正面)或批评(负面)类型。本文对通过应用朴素贝叶斯(NB)和支持向量机(SVM)分类算法获得的结果进行了比较。这些算法用于对具有正面评论或负面评论的情感评论进行分类。基于极性电影数据集标记了用于训练和测试模型的数据集,并与现有文献中的结果进行了比较,以进行严格的检查。

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