Abstract
With the rapid growth of Text sentiment analysis, the demand for automatic classification of electronic documents has increased by leaps and bound. The paradigm of text classification or text mining has been the subject of many research works in recent time. In this paper we propose a technique for text sentiment classification using term frequency- inverse document frequency (TF-IDF) along with Next Word Negation (NWN). We have also compared the performances of binary bag of words model, TF-IDF model and TF-IDF with next word negation (TF-IDF-NWN) model for text classification. Our proposed model is then applied on three different text mining algorithms and we found the Linear Support vector machine (LSVM) is the most appropriate to work with our proposed model. The achieved results show significant increase in accuracy compared to earlier methods.
Abstract (translated)
随着文本情感分析的快速发展，电子文档自动分类的需求也有了飞跃式的增长。文本分类或文本挖掘的范例近来一直是许多研究工作的主题。在本文中，我们提出了一种使用术语频率逆文档频率（TF-IDF）和下一个词否定（NWN）的文本情感分类技术。我们还比较了文字分类模型，TF-IDF模型和TF-IDF的二进制包与下一个词否定（TF-IDF-NWN）模型的性能。我们提出的模型然后应用于三种不同的文本挖掘算法，我们发现线性支持向量机（LSVM）是最适合我们提出的模型。与早期方法相比，实现的结果显示精确度显着提高。
URL
https://arxiv.org/abs/1806.06407
