Abstract
The web is loaded with textual content, and Natural Language Processing is a standout amongst the most vital fields in Machine Learning. But when data is huge simple Machine Learning algorithms are not able to handle it and that is when Deep Learning comes into play which based on Neural Networks. However since neural networks cannot process raw text, we have to change over them through some diverse strategies of word embedding. This paper demonstrates those distinctive word embedding strategies implemented on an Amazon Review Dataset, which has two sentiments to be classified: Happy and Unhappy based on numerous customer reviews. Moreover we demonstrate the distinction in accuracy with a discourse about which word embedding to apply when.
Abstract (translated)
网络上装有文本内容，自然语言处理是机器学习中最重要的领域中的佼佼者。但是当数据非常庞大时，简单的机器学习算法无法处理它，那就是深度学习基于神经网络发挥作用的时候。然而，由于神经网络无法处理原始文本，我们必须通过一些不同的字嵌入策略来改变它们。本文演示了在亚马逊评论数据集上实施的那些独特的词汇嵌入策略，该策略有两种情感可分类：基于众多客户评论的快乐和不快乐。此外，我们证明了准确性与关于哪个词嵌入适用时的话语的区别。
URL
https://arxiv.org/abs/1807.02471
