TF-IDFC-RF：一种新的有监督项加权方案
TF-IDFC-RF: A Novel Supervised Term Weighting Scheme
摘要：
情感分析是情感计算的一个分支，通常被认为是一个二元分类任务。在这条推理路线中，情感分析可以应用于多种上下文，以对文本样本中表达的态度进行分类，例如，电影评论，讽刺等等。表示文本样本的一种常见方法是使用向量空间模型来计算由项的权重组成的数值特征向量。最流行的术语加权方案是TF-IDF（术语频率-逆文档频率）。它是一种无监督加权方案（UWS），因为它在对术语进行加权时不考虑类别信息。除此之外，还有监督加权方案（SWS），它考虑了术语加权计算的类别信息。最近已经提出了几个SWS，表现出比TF-IDF更好的结果。在这种情况下，这项工作提出了不同的术语加权方案的比较研究，并提出了一种新的监督术语加权方案，命名为TF-IDFC-RF（术语频率-类中的逆文档频率-相关频率）。TF-IDFC-RF的有效性进行了验证支持向量机（SVM）和NB（朴素贝叶斯）分类器上的四个常用的情感分析数据集。TF-IDFC-RF显示了有希望的结果，在两个数据集上优于所有其他加权方案。
英文摘要：
Sentiment Analysis is a branch of Affective Computing usually considered a binary classification task. In this line of reasoning, Sentiment Analysis can be applied in several contexts to classify the attitude expressed in text samples, for example, movie reviews, sarcasm, among others. A common approach to represent text samples is the use of the Vector Space Model to compute numerical feature vectors consisting of the weight of terms. The most popular term weighting scheme is TF-IDF (Term Frequency - Inverse Document Frequency). It is an Unsupervised Weighting Scheme (UWS) since it does not consider the class information in the weighting of terms. Apart from that, there are Supervised Weighting Schemes (SWS), which consider the class information on term weighting calculation. Several SWS have been recently proposed, demonstrating better results than TF-IDF. In this scenario, this work presents a comparative study on different term weighting schemes and proposes a novel supervised term weighting scheme, named as TF-IDFC-RF (Term Frequency - Inverse Document Frequency in Class - Relevance Frequency). The effectiveness of TF-IDFC-RF is validated with SVM (Support Vector Machine) and NB (Naive Bayes) classifiers on four commonly used Sentiment Analysis datasets. TF-IDFC-RF shows promising results, outperforming all other weighting schemes on two datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
