International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 05 | May 2024
p-ISSN: 2395-0072
www.irjet.net
Mobility service analysis using machine learning in Python 1.Harshal S. Hemane, 2. Ansh Sahu, 3. Ankush Singh, 4. Anshuman Raj 1Assistant Professor, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India 2,3,4Student, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India
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Abstract -
In an era marked by rapid urbanization and technological advancements, mobility services have emerged as fundamental components of modern urban landscapes. These services, encompassing ridesharing, bike-sharing, public transit, and more, play a pivotal role in facilitating convenient and efficient transportation solutions for individuals and communities alike. However, amidst the proliferation of mobility options, ensuring optimal service quality, user satisfaction, and operational efficiency presents ongoing challenges for service providers.
In the contemporary era of urbanization, mobility services have become integral to daily life, offering convenient transportation solutions. This study presents a comprehensive analysis of mobility services, leveraging the power of machine learning techniques. Specifically, sentiment analysis, time series analysis, and multinomial Naive Bayes (NB) methodology are employed to delve into the intricacies of user experiences and service performance. The sentiment analysis component explores the sentiment polarity of user reviews and feedback pertaining to mobility services. By employing natural language processing (NLP) techniques, sentiment trends are identified, offering insights into customer satisfaction, concerns, and preferences. These sentiments serve as valuable indicators for service providers to enhance user experiences and address areas of improvement.
To address these challenges, this study embarks on a comprehensive analysis of mobility services, harnessing the capabilities of machine learning techniques. In particular, the integration of sentiment analysis, time series analysis, and multinomial Naive Bayes (NB) methodology offers a multifaceted approach to unraveling the complexities inherent in user experiences and service dynamics.
Furthermore, time series analysis is employed to discern patterns and trends in mobility service usage over time. By analyzing historical data, seasonality, trends, and anomalies are identified, enabling forecast models to predict future service demand and optimize resource allocation. This temporal perspective provides valuable foresight for service providers to efficiently manage fleets and infrastructure.
The escalating volume of user-generated content, including reviews, feedback, and social media interactions, provides a rich source of data for understanding user sentiments towards mobility services. Leveraging sentiment analysis techniques, this study seeks to extract valuable insights from unstructured textual data, shedding light on the varying degrees of user satisfaction, concerns, and preferences.
Additionally, the multinomial Naive Bayes methodology is applied to classify user sentiments and feedback into distinct categories. By leveraging probabilistic algorithms, this approach categorizes sentiments into predefined classes, such as positive, negative, or neutral. This classification enables a deeper understanding of user perceptions and facilitates targeted interventions to improve service quality.
Moreover, the temporal dimension of mobility service usage is explored through time series analysis, which aims to uncover underlying patterns, trends, and fluctuations in service demand over time. By analyzing historical data, this approach enables the development of predictive models to anticipate future demand patterns, facilitating proactive resource allocation and service optimization.
Through the integration of sentiment analysis, time series analysis, and multinomial Naive Bayes methodology, this study offers a holistic framework for analyzing and optimizing mobility services. By leveraging machine learning techniques, service providers can gain actionable insights to enhance user satisfaction, operational efficiency, and overall service performance. This research contributes to the advancement of data-driven decision-making in the realm of urban mobility, fostering sustainable and inclusive transportation ecosystems.
Furthermore, the application of multinomial Naive Bayes methodology offers a probabilistic framework for classifying user sentiments and feedback into distinct categories. Through this classification process, sentiments are categorized as positive, negative, or neutral, providing a structured framework for understanding user perceptions and informing targeted interventions for service improvement. By synthesizing these methodologies, this study endeavors to provide a comprehensive framework for analyzing and optimizing mobility services. Through the lens of machine
Key Words: sentiment analysis, Multinomial Naive Bayes (NB), Time Series analysis, Natural Language Processing
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