A Sentiment Analysis Dataset Preparation Framework: A Case Study on User Reviews Dataset on Iranian Tourist Destinations

The tourism industry has undergone a significant shift towards data-driven strategies in recent years. As a means of improving the quality of their service and performance, service providers are analyzing feedback from their customers to increase the number of tourists they attract. Negative feedback also provides valuable insights into the factors that detract from a location's appeal. Datasets that gather information on people's experiences and opinions of tourist destinations can be analyzed to extract valuable information. However, there are currently few existing datasets that specifically capture user reviews about historical and tourist attractions in Iran. To fill this gap, users have shared their travel experiences on various websites, and sentiment analysis can be employed to extract insights from this data. Effective sentiment analysis requires a suitable approach for data extraction, pre-processing, and storage. This study provides a framework for the user review dataset preparation, including data collection, ETL, data storage, and evaluation phases. A rich dataset containing user reviews about 178 Iran's historical and tourist attractions was prepared through the proposed framework in which automated crawlers were developed to collect data from Tripadvisor platforms. Data labelling was achieved using the DistilBERT-base-uncased language model for sentiment analysis and human evaluators for final annotations. A total of approximately 25 thousand samples were included in the dataset, and positive user comments outnumbered negative user comments by a wide margin. This high percentage of positive comments suggests that the locations were of a satisfactory standard, making it likely that users would return in the future. The findings of this study can help providers to improve the overall quality of their services by analyzing user reviews. The proposed framework and achieved dataset can also guide future efforts to leverage data for improved performance and customer satisfaction in the tourism industry by identifying areas that need improvement.