Developing A Cloud-Based Natural Language Processing (NLP) Platform for Sentiment Analysis and Opinion Mining of Social Media Data
Keywords:
natural language processing; sentiment analysis; opinion mining; cloud computing; microservices; Kubernetes; deep learning; social media analyticsAbstract
With the rapid growth of user-generated content on social media platforms, there is an increasing need for efficient and scalable natural language processing (NLP) tools to analyze and derive insights from this vast amount of textual data. Sentiment analysis and opinion mining are two crucial NLP tasks that enable businesses, organizations, and researchers to understand public opinion, monitor brand reputation, and make data-driven decisions. This paper presents the development of a cloud-based NLP platform that leverages state-of-the-art deep learning models and big data technologies to perform large-scale sentiment analysis and opinion mining on social media data. The proposed platform utilizes a microservices architecture deployed on a Kubernetes cluster, enabling high scalability, fault-tolerance, and easy integration with other systems. We evaluate the performance of the platform on multiple benchmark datasets and real-world social media data, demonstrating its effectiveness in accurately classifying sentiment polarity and extracting key opinion targets and aspects. The platform achieves an average F1-score of 0.87 for sentiment classification and 0.81 for aspect-based opinion mining. We also conduct a case study to showcase the platform's ability to monitor and analyze public opinion on a specific topic over time. The results highlight the potential of the proposed cloud-based NLP platform in facilitating data-driven decision making and providing valuable insights from social media data.
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