Sentiment Analysis of Weibo Comment Texts Based on Extended Vocabulary and Convolutional Neural Network

被引：13
|
作者：
Yang, Xiaoyilei [1 ]
Xu, Shuaijing [1 ]
Wu, Hao [1 ]
Bie, Rongfang [1 ]
机构：
[1] Beijing Normal Univ, Beijing 100875, Peoples R China
来源：
2018 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS | 2019年 / 147卷
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Weibo sentiment analysis; convolution neural network; extended vocabulary; word2vec;
D O I：
10.1016/j.procs.2019.01.239
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In the era of big data and Internet, social network platforms, blogs, and recommender systems generate thousands of subjective information every day. The emotional content of these information may be related to books, characters, commodities, activities and so on. Analyzing and mining subjective emotional information is conducive to personal decision making, enterprise reform, and government's public opinion regulation. In this paper, based on Weibo comment texts, we use the network term and the wiki Chinese data set to expand the original vocabulary, train word embeddings and realize the sentence-level sentiment classification based on the convolution neural network. At the same time, an optimization method according to the statement length of pooling layer is put forward. The method is proved to be effective with high accuracy on our data set. (C) 2019 The Authors. Published by Elsevier B.V.
引用
收藏
页码：361 / 368
页数：8
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