# Unlocking the Potential of Keyword Extraction: The Need for Access to High-Quality Datasets
## 5 Discussion  
This study has addressed the issue of inadequate datasets for keyword extraction methods. The absence of a suitable dataset could impede researchers from fully exploring the scope of semantic analysis for keyword extraction. It could also make it challenging to evaluate the performance of keyword extraction models, compare different approaches, and identify areas for improvement. Additionally, the unavailability of a suitable dataset could make it difficult to develop keyword extraction models that are relevant across diverse domains and use cases.  
In this study, we have evaluated two datasets, Twitter and Mohler, based on their structure, complexity, and quality. This is an important step, as the quality of the dataset can greatly impact the performance of the models used for keyword extraction. By selecting a suitable dataset, a model can generate reliable and accurate results. After evaluating the datasets, we applied three different models, KeyBERT, YAKE, and RAKE, to extract keywords from the data. The results show that YAKE performs better on the Twitter dataset, whereas KeyBERT outperforms YAKE on the Mohler dataset. RAKE has a lower score compared to the other two models. This information can help researchers and practitioners select the most appropriate model for their specific dataset.  
Furthermore, we have developed a general preprocessing pipeline for the three models to enhance the accuracy of keyword extraction. This is an important step, as preprocessingcan significantly impact the quality of the results. By developing a preprocessing pipeline, researchers can ensure that the models are working with clean and consistent data, leading to more accurate results.  
In addition to evaluating the performance of the three models, KeyBERT, YAKE, and RAKE, we have also examined their limitations. This is an important aspect to consider, as it helps to understand the potential drawbacks of each model and the situations in which they may not perform optimally.  
KeyBERT, for instance, relies heavily on pre-trained language models, which may not always capture the domain-specific context of the dataset being analyzed. This could lead to the extraction of less relevant keywords or the omission of important ones.  
YAKE, on the other hand, relies on statistical properties of the dataset, such as term frequency and co-occurrence, which can be affected by noisy data or sparse datasets. This can result in the extraction of irrelevant or noisy keywords.  
RAKE uses a simple and unsupervised approach to keyword extraction that relies on the extraction of frequent phrases from the text. However, this approach may not always capture the nuances of the text and may result in the extraction of uninformative phrases or stop words.  
Overall, while the three models have shown promise in the context of research, it is important to keep their limitations in mind when selecting a model for a specific dataset or application. By understanding the limitations of each model, one can make an informed decision about which model to use and how to optimize its performance for the specific use case.