Classification of Tweets Into Facts and Opinions Using Recurrent Neural Networks

被引:0
作者
Pattusamy, Murugan [1 ]
Kanth, Lakshmi [1 ]
机构
[1] Univ Hyderabad, Hyderabad, India
关键词
Classification of Tweets; Long Short-Term Memory Model; Recurrent Neural Networks; EVENT DETECTION; SOCIAL MEDIA; TWITTER;
D O I
10.4018/IJTHI.319358
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
In the last few years, the growth rate of the number of people who are active on Twitter has been consistently spiking. In India, even the government agencies have started using Twitter accounts as they feel that they can get connected to a greater number of people in a short span of time. Apart from the social media platforms, there are an enormous number of blogging applications that have popped up providing another platform for the people to share their views. With all this, the authenticity of the content that is being generated is going for a toss. On that note, the authors have the task in hand of differentiating the genuineness of the content. In this process, they have worked upon various techniques that would maximize the authenticity of the content and propose a long short-term memory (LSTM) model that will make a distinction between the tweets posted on the Twitter platform. The model in combination with the manually engineered features and the bag of words model is able to classify the tweets efficiently.
引用
收藏
页码:1 / 14
页数:14
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