## 2 Background
### Text Mining for ANN  
Design research has been increasingly attracting attention among researchers in recent years, with many researchers working to develop new and advanced approaches for studying design phenomena. One such approach is \"text mining\", which uses computational techniques to extract meaningful information from large amounts of unstructured text data. This study is one of the first to apply text mining to design research publications, with the aim of analyzing the trends and patterns in the field and identifying future research directions [37; 38; 39; 40]. Text mining is a process of extracting meaningful and relevant information from unstructured text data [41; 42]. It has been applied in the field of ANNs to improve the performance of natural language processing tasks, such as sentiment analysis, text classification, and named entity recognition. By using text-mining techniques, such as natural language processing, text classification, and sentiment analysis, researchers can preprocess text data and convert it into a format that can be easily processed by ANNs.  
Recent research has shown that text-mining techniques can improve the performance of ANNs in various natural language processing tasks. For instance, [PERSON] et al. [43] applied text-mining techniques to improve the performance of sentiment analysis by extracting features such as word embeddings and part-of-speech tags. [PERSON] et al. [44] proposed an approach that combines text-mining techniques with ANNs to improve the performance of text classification. [PERSON] et al. [45] combined text mining and ANNs to  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Advantages** & **Disadvantages** & **Limitations** \\ \hline High accuracy in modeling complex systems & Overfitting & Sensitivity to noise and irrelevant inputs \\ \hline Capable of handling large amounts of data & Slow convergence in training & Lack of interpretability \\ \hline Can handle non-linear relationships & Prone to local minima & Requires careful selection of architecture and hyperparameters \\ \hline Can perform unsupervised learning & Can be computationally intensive & Can be sensitive to the choice of activation function \\ \hline Can perform feature extraction & Can be prone to over-fitting & Requires large amounts of computational resources \\ \hline Can handle imbalanced data & Can be prone to over-training & Requires extensive preprocessing and feature engineering \\ \hline Can model complex, non-linear relationships & Can be sensitive to the choice of loss function & Requires careful initialization of weights \\ \hline Can perform online learning & Can have poor generalization performance & Can be sensitive to the choice of optimization algorithm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages, Disadvantages, and Limitations of ANN-based Approaches.  
achieve state-of-the-art performance in named entity recognition. [PERSON] et al. [46] combined text mining and ANNs for opinion mining and sentiment analysis. [PERSON] et al. [47] proposed a hybrid approach of combining text-mining and ANNs to improve the performance of stock price prediction.  
In summary, previous studies in the field of design research have focused mainly on identifying potential areas for future investigation and have covered a wide range of topics and approaches. However, there is currently a lack of research that synthesizes the existing literature and provides recommendations for future research directions. This study aims to fill this gap by using text mining to analyze design research publications and identify key themes and trends in the field. The following paragraphs provide a detailed description of the research methods and data analyses used in this study.