Measuring Customer Sentiment on Twitter
Social media analytics
Sentiment analysis
Customer engagement
Actionable insights
Business growth
Objective
The primary objective of this case study was to develop a robust system to monitor and analyse customer sentiment expressed on Twitter. By harnessing the power of natural language processing and machine learning techniques, we aimed to provide our client with valuable insights into customer perception, sentiment trends and potential areas for improvement.
CLIENT BACKGROUND
Our client, a leading consumer goods company, sought to gain deeper insights into customer sentiment surrounding their brand and products. They recognised the immense potential of social media and specifically Twitter as a platform to capture real-time opinions and emotions. We were engaged to design and implement a comprehensive solution for measuring customer sentiment on Twitter.
OUR APPROACH
We collected Twitter data and used natural language processing techniques to extract sentiment and insights. By correlating this sentiment data with key events, we identified trends and patterns in customer sentiment. Applying advanced machine learning algorithms, we built predictive models to accurately forecast sentiment fluctuations. This provided our client with actionable insights to make informed decisions, enhance customer engagement and drive business growth.
