Leveraging Machine Learning Algorithms to Analyze Social Media Data for Pain Point Identification

EasyChair Preprint 13776

Abstract

Social media platforms have become a rich source of valuable information for businesses, providing insights into customer sentiments, preferences, and pain points. Identifying these pain points is crucial for organizations to enhance customer satisfaction, improve products or services, and drive business growth. Leveraging machine learning algorithms for analyzing social media data offers a powerful solution to extract meaningful patterns and identify pain points at scale. This abstract provides an overview of the process involved in leveraging machine learning algorithms for pain point identification based on social media data. It covers aspects such as data collection, preprocessing, exploratory data analysis, feature extraction, selecting appropriate machine learning algorithms, training and testing, pain point identification, and deriving actionable insights. The abstract also highlights the challenges and limitations associated with this approach, emphasizing ethical considerations and potential biases. By harnessing the power of machine learning, businesses can gain valuable insights from social media data, enabling them to address pain points effectively and make informed decisions to enhance customer satisfaction.

Keyphrases: Prioritization, Root Causes, customer needs, customer satisfaction, data analysis, solutions

@booklet{EasyChair:13776,
  author    = {Edwin Frank and Samon Daniel},
  title     = {Leveraging Machine Learning Algorithms to Analyze Social Media Data for Pain Point Identification},
  howpublished = {EasyChair Preprint 13776},
  year      = {EasyChair, 2024}}