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Deep text clustering using stacked AutoEncoder
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AI
DOI：10.1007/s11042-022-12155-0.png)
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Abstract
En 中文
Text data is a type of unstructured information, which is easily processed by a human, but it is hard for the computer to understand. Text mining techniques effectively discover meaningful information from text, which has received a great deal of attention in recent years. The aim of this study is to evaluate and analyze the comments and suggestions presented by Barez Iran Company. Barez is an unlabeled dataset. Extracting useful information from unlabeled large textual data by human to manually be very difficult and time consuming. Therefore, in this paper we analyze suggestions presented in Persian using BERTopic modeling for cluster analysis of the dataset. In BERTopic, each document belongs to a topic with a probability distribution. As a result, seven latent topics are found, covering a broad range of issues such as Installation, manufacture, correction, and device. Then we propose a novel deep text clustering based on hybrid of a stacked autoencoder and k-means clustering to organize text documents into meaningful groups for mining information from Barez data in an unsupervised method. Our data clustering has three main steps: 1) Text representation with a new pre-trained BERT model for language understanding called ParsBERT, 2) Text feature extraction based on based on a new architecture of stacked autoencoder to reduce the dimension of data to provide robust features for clustering, 3) Cluster the data by k-means clustering. We employ the Barez dataset to verify our work's effectiveness; Silhouette Score is used to evaluate the resulting clusters with the best value of 0.60 with 3 clusters grouping. Experimental evaluations demonstrate that the proposed algorithm clearly outperforms other clustering methods.
Keywords:
AutoEncoder
Clustering
BERT word embedding
Topic modelling
Journal
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