Leveraging Cloud Based Non-Mapreduce Big Data Analytics for Predicting Consumer Behavior
Keywords:
Social Media Data, Consumer Behavior, Predictive Analytics, Non-MapReduce Computing, Cloud Based AnalyticsAbstract
In marketing analysis, understanding consumer behavior is crucial for businesses to stay competitive. Social media platforms have emerged as rich sources of data that can offer insights into consumer perceptions, sentiments, and preferences. However, analyzing this vast and unstructured data poses significant challenges, necessitating advanced analytical techniques and scalable computing frameworks. Traditional approaches to analyzing social media data often struggle with scalability and efficiency, particularly when dealing with large datasets across diverse platforms. Extracting meaningful insights from unstructured text data requires sophisticated natural language processing (NLP) techniques. Addressing these challenges is essential for businesses seeking to leverage social media data for predictive analytics and informed decision-making. This research proposes a novel framework for predicting consumer behavior using cloud based non-MapReduce big data analytics. The framework integrates Named Entity Recognition NLP algorithm with non-MapReduce computing techniques to analyze social media datasets from platforms such as Facebook, LinkedIn, YouTube, Instagram, Pinterest, and Snapchat. By partitioning the data into random files and executing iterative algorithms on distributed clusters, the framework enables efficient processing of large-scale datasets while preserving data integrity and scalability. Experimental validation of the proposed framework shows its efficacy in predicting consumer behavior with high accuracy. Using a collected dataset comprising millions of social media interactions, the framework achieved a sentiment prediction accuracy of over 90%.
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