Abstract
The advancement in technology is taking place with an accelerating pace across the globe. With the increasing expansion and technological advancement, a vast volume of text data are generated everyday, in the form of social media platform, websites, company data, healthcare data, and news. Indeed, it is a difficult task to extract intriguing patterns from the text data, such as opinions, summaries, and facts, having varying length. Because of the problems of the length of text data and the difficulty of feature value extraction in news, this paper proposes a news text classification method based on the combination of deep learning (DL) algorithms. In order to classify the text data, the earlier approaches use a single word vector to express text information and only the information of the relationship between words were considered, but the relationship between words and categories was ignored which indeed is an important factor for the classification of news text. This paper follows the idea of a customized algorithm which is the combination of DL algorithms such as CNN, LSTM, and MLP and proposes a customized DCLSTM-MLP model for the classification of news text data. The proposed model is expressed in parallel with word vector and word dispersion. The relationship among words is represented by the word vector as an input of the CNN module, and the relationship between words and categories is represented by a discrete vector as an input of the MLP module in order to realize comprehensive learning of spatial feature information, time-series feature information, and relationship between words and categories of news text. To check the stability and performance of the proposed method, multiple experiments were performed. The experimental results showed that the proposed method solves the problems of text length, difficulty of feature extraction in the news text, and classification of news text in an effective way and attained better accuracy, recall rate, and comprehensive value as compared to the other models.
Highlights
News has evolved into an institution that disseminates the most up-to-date information to the public
At the moment, news is still manually classified into these categories, which means that when submitting a news item, the submitter would first know the entire text of the news to be published, which is placed in the appropriate category. is is a very tough task for the news unloaders who have a high amount of news items to deal with
The abovementioned research of combination depth learning uses a single word vector to express text information and only the information of the relationship between words is considered, but the relationship between words and categories is ignored which is an important factor for the classification of news text. e following are some of the basic contributions of this paper: (i) is study follows the research idea of the combination of deep learning (DL) techniques based news text classification and selects CNN, long short-term memory (LSTM), and models in the percentage (MLP) models to propose a custom MLP model of news text classification based on double input combined depth learning
Summary
News has evolved into an institution that disseminates the most up-to-date information to the public. One of the important functionality of text mining is that it identifies the most important patterns in a large amount of text dataset It is used for clustering, feature extraction, and information retrieval. The abovementioned research of combination depth learning uses a single word vector to express text information and only the information of the relationship between words is considered, but the relationship between words and categories is ignored which is an important factor for the classification of news text. (i) is study follows the research idea of the combination of DL techniques based news text classification and selects CNN, LSTM, and MLP models to propose a custom MLP model of news text classification based on double input combined depth learning.
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