# Developing Data-Conscious Deep Learning Models for Product Classification
## 3 Proposed Methods  
### Model Architecture and Dataset Details  
Our proposed product classification model is based on an approach that integrates the data selection, transformation, and filtering phases with the inputs of the deep learning model used. Figure 1 illustrates an overview of this architecture. The selection phase involves the selection of appropriate input data as training data, and the transformation phase involves the structural (rather than semantic) transformation of the selected data for their convenient application in training. For example, we (1) removed null data, duplicate data, etc. ; (2) removed special characters, emojis, etc., except letters; and (3) used a morphological analyzer to extract only meaningful words or characters as outputs. After the transformation phase, the filtering process is performed to filter out and remove the stopwords (which are considered to be unnecessary for training) among the words. Upon completion of the transformation phase, ancillary tasks such as tokenization, padding, or tensorization are performed. Finally, the results are used as the input data to train the deep learning models. Various deep learning models can be employed. The models we used are presented in the next section. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Works** & **Models or Classifiers** & **Approach** \\ \hline [PERSON] et al. [3] & Multi-CNN, Multi-LSTM & Develop a new model \\ \hline [PERSON], [PERSON] et al. [4] & CNN, GBT, ACNN & Develop a new model \\ \hline [PERSON] et al. [1] & [PERSON], CNN, GBT & Hyperparameter tuning \\ \hline [PERSON] [2] & LSTM & Hyperparameter tuning \\ \hline [PERSON], [PERSON] et al. [20] & DeepCNN & Develop a new model \\ \hline [PERSON], [PERSON] et al. [8] & VDCNN, AbLSTM & Hyperparameter tuning \\ \hline [PERSON], [PERSON] et al. [21] & CNN, VGG & Develop a new model \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Studies related to deep learning models for product classification. Figure 1: Architecture of our data-conscious product classification model: incorporated with the selection, transformation, and filtering phases. In this study, we used real-world data provided by a South Korean used-product trading platform company [12]. The data contained the information of approximately 2.9 million products the second half of 2019. The product information data comprised the attributes \"name\", \"keyword\", and \"description\" in the text format, and \"category\" in the integer format. The attributes \"name\", \"keyword\", and \"description\" provided each product's name, keyword, and description, respectively. Meanwhile, the attribute \"category\" provided the product classification number to which each product belonged to. For the product classification, we used the second-level classification of a commonly used three-level hierarchy system [1,4]. The total number of second-level categories was around 120. Note that we can have seven ways of simply combining the training data for the selection process, as there are three attributes of data: \"name\", \"keyword\", and \"description\". Figure 2 shows the distribution of the number of product data by category in descending order. It is distributed disproportionately across the categories, similar to other datasets [1,2,4]. Figure 3 shows the distribution of the number of words for the name, keyword, and description attributes. In the figure, the distributions before and after the transformation phase are represented by blue and orange, respectively. The blue and red vertical lines represent the average number of words per product data for each attribute before and after the transformation phase, respectively. After the process, the average number of words for the name, keyword, and description attributes varied to 5.2, 6.6, and 39.9, respectively. In the case of the name and keyword attributes, the number of words increased because these were split into nouns through the transformation process.