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首页> 外文期刊>Journal of Theoretical and Applied Information Technology >THE MULTI-DIMENSIONAL VECTORS AND AN YULE-II MEASURE USED FOR A SELF-ORGANIZING MAP ALGORITHM OF ENGLISH SENTIMENT CLASSIFICATION IN A DISTRIBUTED ENVIRONMENT
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THE MULTI-DIMENSIONAL VECTORS AND AN YULE-II MEASURE USED FOR A SELF-ORGANIZING MAP ALGORITHM OF ENGLISH SENTIMENT CLASSIFICATION IN A DISTRIBUTED ENVIRONMENT

机译：分布式矢量环境中英语情感分类的自组织映射算法的多维矢量和YULE-II度量

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We have proposed a new model for big data sentiment classification using a Self-Organizing Map Algorithm (SOM) ? an unsupervised learning of a machine learning to classify the sentiments (positive, negative, or neutral) for all the documents of our testing data set according to all the documents of our training data set in English. We only run the SOM only once, the results of the sentiment classification of all the documents of the testing data are identified. The SOM is proposed according to many multi-dimensional vectors of both the testing data set and the training data set. The multi-dimensional vectors are based on many sentiment lexicons of our basis English sentiment dictionary (bESD). One document is corresponding to one multi-dimensional vector according to the sentiment lexicons. After running the SOM only once, a Map is used in presenting the results of the SOM. The results of clustering all documents of the testing data set into either the positive polarity or the negative polarity are shown on the Map, we can find all the results of the sentiment classification of all the documents of the testing data set fully. We only use many multi-dimensional vectors based on the sentiment lexicons of the bESD. In a sequential system, the new model has been tested firstly, and then, this model has been performed in a parallel network environment secondly. The accuracy of the testing data set has been achieved 88.72% certainly. Many different fields can widely use the results of this new model.
机译：我们提出了一种使用自组织映射算法（SOM）进行大数据情感分类的新模型。机器学习的无监督学习，可根据英语中的训练数据集的所有文档对测试数据集的所有文档的情绪（正面，负面或中性）进行分类。我们仅运行SOM一次，所有测试数据文档的情感分类结果均被识别。根据测试数据集和训练数据集的许多多维向量，提出了SOM。多维矢量基于我们基础英语情感词典（bESD）的许多情感词典。根据情感词典，一个文档对应于一个多维矢量。仅运行一次SOM之后，将使用一个Map呈现SOM的结果。地图上显示了将测试数据集的所有文档聚类为正极性或负极性的结果，我们可以完全找到测试数据集的所有文档的情感分类的所有结果。我们仅基于bESD的情感词典使用许多多维向量。在顺序系统中，首先测试了新模型，然后在并行网络环境中执行了该模型。测试数据集的准确性肯定达到了88.72％。许多不同领域可以广泛使用此新模型的结果。

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