| 基于卷积神经网络的英文篇章情感量化方法 |
| |
| 作者单位： | ;1.信阳农林学院外国语学院;2.信阳农林学院信息工程学院;3.信阳师范学院计算机与信息技术学院/河南省教育大数据分析与应用重点实验室 |
| |
| 摘 要： | 提出利用卷积神经网络(CNN)预测英文单词情感极性,并利用英文单词情感极性设计量化篇章情感倾向的方法.首先,利用fastText技术训练词嵌入模型,将英文单词转化为定长、稠密的词向量;接着,以词向量作为输入,构造一维CNN模型,并设计出多种具有不同深度的架构;最后,利用CNN预测模型计算篇章中所含英文单词的平均情感极性作为篇章情感倾向的量化分值.实验结果表明:相比于传统的机器学习模型,提出的CNN预测模型能够提升英文单词情感预测精度,所设计的篇章情感量化方法,也与主观判决情感色彩有较好的一致性.
|
| 关 键 词： | 情感分析 机器学习 卷积神经网络 词嵌入 情感量化 |
Sentiment Quantization of English Texts Based on Convolutional Neural Networks |
| |
| Institution: | ,School of Foreign Languages,Xinyang Agriculture and Forestry University,School of Information Engineering,Xinyang Agriculture and Forestry University,College of Computer and Information Technology/Henan Key Lab of Analysis and Applications of Education Big Data,Xinyang Normal University |
| |
| Abstract: | Convolutional Neural Networks(CNN)is used to predict the sentiment polarities of English words,and based on these polarities,a method is designed to quantify textual sentiment tendency.First,the fastText tool is utilized to train the word embedding model which transforms words to fixed-length dense word vectors.Then,taking the word vectors as input,one-dimensional CNN model is constructed and multiple model frameworks are also designed with different depths.Finally,CNN model is used to compute the mean of the sentiment polarity of the words in an English text,and the mean would be the quantized value of the text sentiment tendency.Experimental results show that the proposed CNN model improves the accuracy of predicting word sentiment when compared with some traditional machine learning models,and the proposed quantification method of text sentiment also presents a good consistence with the subjective results. |
| |
| Keywords: | sentiment analysis machine learning convolutional neural networks word embedding sentiment quantification |
|
|
