论文标题
波斯社交媒体中的情感分析的卷积神经网络
Convolutional Neural Networks for Sentiment Analysis in Persian Social Media
论文作者
论文摘要
随着社交媒体参与的兴起，所得数据可以用作分析和理解我们周围不同现象的丰富资源。情感分析系统采用这些数据来找到社交媒体用户对给定文档中某些实体的态度。在本文中，我们提出了一种使用卷积神经网络（CNN）的情感分析方法，即一种前馈性人工神经网络，该方法通过应用不同过滤器的卷积过度输入数据，将句子分为两个和五个类（考虑其强度）。我们使用曲线指标下的区域在波斯社交媒体文本的三个不同数据集上评估了该方法。最终结果表明，使用CNN优于早期尝试开发传统的机器学习方法对波斯文字分类，尤其是短文。
With the social media engagement on the rise, the resulting data can be used as a rich resource for analyzing and understanding different phenomena around us. A sentiment analysis system employs these data to find the attitude of social media users towards certain entities in a given document. In this paper we propose a sentiment analysis method for Persian text using Convolutional Neural Network (CNN), a feedforward Artificial Neural Network, that categorize sentences into two and five classes (considering their intensity) by applying a layer of convolution over input data through different filters. We evaluated the method on three different datasets of Persian social media texts using Area under Curve metric. The final results show the advantage of using CNN over earlier attempts at developing traditional machine learning methods for Persian texts sentiment classification especially for short texts.
