Leveraging AI to Analyze Sentiment and Derive Insights from Social Media Conversations

Case Study

Leveraging AI to Analyze Sentiment and Derive Insights from Social Media Conversations

Business Objective

Our client, headquartered in Singapore, is a leading food and agri-business supplier operating from seed-to-shelf in about 70 countries and is also among the leading suppliers of coffee worldwide.

The client wanted to understand the sentiment and brand engagement for specialty coffee through social media analytics. The project involved identifying popular themes and sentiments around specialty coffee and deploy a fully automated dashboard to visualize the results.

Challenges

  • Identifying and extracting data from social media for 1,000+ customers across different platforms like Facebook and Twitter
  • Distinguishing between the general coffee conversations and getting relevant insights only on “Specialty Coffee”

Solution Methodology 

  • Used APIs and RSS feeds to scrape and extract data from Twitter and Facebook
  • Parsed and cleaned data to store into ready-to-access data tables
  • Developed business logic in discussion with the client to tag general coffee and specialty coffee content
  • Deployed machine learning algorithms to analyze the text data and understand the sentiment associated with the extracted feed at specialty coffee type and region levels
  • Stored the analyzed results in a format suitable for appropriate Tableau visualizations and management actions

Business Impact

 

  • Measured brand engagement, sentiment trends, and identified popular flavors for specialty coffee across geographies
  • Developed a fully automated dashboard refreshed regularly from data extraction to insights
  • Depth of insights enabled the management to take steps to build brand engagement and understand the competitive landscape
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