# Integrating Text Classification into Topic Discovery Using Semantic Embedding Models
## 1 Introduction
Therefore, a system that works with many documents requires algorithms or methods to provide the computer with the necessary knowledge to generate the results expected by a user [10]. In addition, _deep learning_ is a process carried out with a neural network, for example, a convolutional neural network. It has been adopted for text classification tasks, generating better results than a traditional classification task [11]. A _CNN_ is a multi-layer network or hierarchical network and is a high-level feature-based method. _CNN_ builds by stacking multiple layers of features. One feature of a _CNN_ is the presence of a subsampling or pooling layer [12]. It allows for optimizing the calculation processes to reduce the data size in learning new data, allowing the recognition of different features [3]. Currently, computational approaches need to model knowledge to generate accurate results without the intervention of a person. _Text classification_ involves ordering large amounts of documents in short periods. On the other hand, _topic discovery_ involves finding the main ideas from large amounts of textual data; it is presented as a recurring topic. The objective of _topic discovery_ in text documents is to extract the central idea by imitatinghuman capacity, without human intervention automatically extracting knowledge from the text. It indicates the recurring topics in the documents, allowing for an overview of the text. A topic discovery model that receives text with or without processing generates general topics since the input data include many unclassified texts. However, discovering topics from previously classified text allows us to learn specific topics. Hence, the top words that characterize the topics are linked to each other since they come from classified text [13]. This paper presents a text classification process integrated into identified topics with semantic embedding models. This incorporation provides specific topics with specific significance instead of general topics from the complete set of unclassified text since the topics are extracted from each identified class. The input texts are composed of two news domain corpora, previously classified with a convolutional neural network, using three semantic embedding models [3] as semantic features. The proposed topic discovery process aims to obtain specific topics for each class with semantic relationships between their top words. A quality assessment of the identified topics was performed with the normalized topic coherence metric. Therefore, the identified topics in each class provide latent and specific topics depicted by top words with high coherence from each obtained class. Based on the results obtained by integrating text classification with topic discovery, it was concluded that discovering topics in previously classified text generates specific topics from each class. The rest of the paper is organized as follows. Section 2 explores works related to this research. Section 3 shows the proposed approach to incorporate _text classification_ in the _topic discovery_ process. The experimental results are presented in Section 4. The conclusions and future work are presented in Section 5.