Sentiment classification of web social media faces the problem of text context semantics missing. The existing research mainly solves the problem of text context semantic missing by mining language symbol information in web social media text, seldom considering the emoticon symbols and punctuation symbols in web social media text. Similar to language symbols, emoticons’ symbols and punctuation symbols in web social media text also contain certain sentiment information. In order to make full use of sentiment information contained in web social media to solve the problem of text context semantics missing, we propose a sentiment classification method of web social media based on multidimensional and multilevel modeling. By modeling web social media text from three dimensions (language symbols, emoticons’ symbols, and punctuation symbols) and three levels (words, sentences, and documents) based on a deep learning framework, in this article, we attempt to solve text context semantics missing faced by the sentiment classification of web social media and improve the accuracy of sentiment classification of web social media. The experimental results on Sina Weibo and Twitter datasets show that the average accuracy of our method is 0.9479, which achieves more than 5.86% performance compared with the existing sentiment classification methods.

中文翻译：

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一种基于多维多层次建模的网络社交媒体情感分类方法

网络社交媒体的情感分类面临文本上下文语义缺失的问题。现有研究主要通过挖掘网络社交媒体文本中的语言符号信息来解决文本上下文语义缺失的问题，很少考虑网络社交媒体文本中的表情符号和标点符号。与语言符号类似，网络社交媒体文本中的表情符号和标点符号也包含一定的情感信息。为了充分利用网络社交媒体中包含的情感信息来解决文本上下文语义缺失的问题，我们提出了一种基于多维多层次建模的网络社交媒体情感分类方法。通过从三个维度（语言符号、表情符号、和标点符号）和基于深度学习框架的三个层次（单词、句子和文档），在本文中，我们试图解决网络社交媒体情感分类面临的文本上下文语义缺失问题，提高情感分类的准确性的网络社交媒体。在新浪微博和 Twitter 数据集上的实验结果表明，我们的方法的平均准确率为 0.9479，与现有的情感分类方法相比，性能提高了 5.86% 以上。