使用监督学习算法的意见挖掘和音乐歌词分类

M. Ahuja, A. L. Sangal
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引用次数: 3

摘要

歌词是歌曲中重要而有意义的组成部分,有助于调查和分类由此产生的意见(情绪)。观点挖掘也被称为情感分析,是一种数据处理领域,用于从文本中找出作者、用户和主观性的观点。在这部作品中,我们只考虑歌曲的英语抒情部分。然后结合世界网络的知识来查找单词的同义词。本研究的目的是对音乐歌词进行语言调查,这些歌曲是否对听众有用,并将其分类为积极和消极的满足。为了评估这一点,我们使用POS标注器对涉及意见(情绪)的单词进行了研究,并使用不同的机器学习算法(监督)随机森林、梯度增强和投票分类器(包括逻辑回归、决策树和支持向量机)将它们分类到情绪类别中,并与不同的参数进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Opinion Mining and Classification of Music Lyrics Using Supervised Learning Algorithms
Music Lyrics is an important and meaningful part of any song that are helpful in investigations and classification of opinion (sentiment) develop from it. Opinion mining is also referred as sentiment analysis is the field of data processing that is used to find out opinion of an author, user and subjectivity from text. In this work we are considering only the English lyrical part of a song. WorldNet knowledge is then incorporate to find out synonyms of words. The Goal of this research is doing a linguistic investigation of music lyrics whether these songs useful for listeners or not and classifying them with positive and negative fulfilled present in them. In Order to evaluate this words involve opinion(sentiment) have been investigate with using POS tagger and classifying them into mood categories using different machine learning algorithms(supervised) Random Forest, Gradient Boosting and Voting Classifier(including logistic regression, Decision Tree and SVM) and compare with different parameters.
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