An analysis of ridesharing trip time using advanced text mining techniques

The time cost of ridesharing rental represents a crucial factor influencing users'decisions to rent a car.Researchers have explored this aspect through text analysis and questionnaires.However,the current research faces limitations in terms of data quantity and analysis methods,preventing the extraction of key information.Therefore,there is a need to further optimize the level of public opinion analysis.This study aimed to investigate user perspectives concerning travel time in ridesharing,both pre and post-pandemic,within the Twitter application.Our analysis focused on a dataset from users residing in the USA and India,with considerations for demographic variables such as age and gender.To accomplish our research objectives,we employed Latent Dirichlet Allocation for topic modeling and BERT for sentiment analysis.Our findings revealed significant influences of the pandemic and the user's country of origin on sentiment.Notably,there was a discernible increase in positive sentiment among users from both countries following the pandemic,particularly among older individuals.These findings bear relevance to the ridesharing industry,offering insights that can aid in establishing benchmarks for improving travel time.Such improvements are instrumental in enabling ridesharing companies to effectively compete with other public transportation alternatives.

Understanding RFID Technology within a Business Intelligence Framework

Radio Frequency Identification (RFID) technology provides new and exciting opportunities for increasing organiza- tional, financial, and operational performance. With its focus on organizational efficiency and effectiveness, RFID technology is superior to barcodes in its ability to provide source automation features that increase the speed and volume of data collection for analysis. Today, applications that employ RFID are growing rapidly and this technology is in a continuous state of evolution and growth. As it continues to progress, RFID provides us with new opportunities to use business intelligence (BI) to monitor organizational operations and learn more about markets, as well as consumer attitudes, behaviors, and product preferences. This technology can even be used to prevent potentially faulty or spoiled products from ending up in the hands of consumers. However, RFID offers significant challenges to organizations that attempt to employ this technology. Most significantly, there exists the potential for RFID to overwhelm data collection and BI analytic efforts if organizations fail to effectively address RFID data integration issues. To this end, the purpose of this article is to explicate the dynamic technology of RFID and how it is being used today. Additionally, this article will provide insights into how RFID technology is evolving and how this technology relates to BI and issues related to data integration. This knowledge has never been more essential. While IT academic research into RFID development and issues has declined in recent years, RFID continues to be a vital area of exploration, especially as it relates to BI in the 21st century.