GenAI - Multilingual Sentiment Analysis of Social Media Content

SUMMARY

We conducted large-scale sentiment analysis on 1 million English and French social media phrases for a client-supplied dataset. Native language experts classified each statement as Positive, Neutral, or Negative. The five-month project focused on accurately interpreting diverse tone, context, and emotion from real user-generated content.

THE CHALLENGE

• Tone and sentiment ambiguity in short-form social media text
• Sarcasm, slang, and idiomatic expressions complicating classification
• Language-specific nuances, especially in multilingual datasets
• Maintaining consistent annotation quality at large scale

SOLUTION

• Onboarded and trained native linguists in both English and French
• Defined robust sentiment classification guidelines with examples
• Layered QC processes with inter-annotator agreement metrics
• Deployed annotation platform with client-aligned taxonomy
• Generated detailed CSV output with sentiment tags for each phrase

KEY OUTCOMES

• Delivered 1 million sentiment-annotated phrases with high accuracy
• Maintained quality using native English and French resources
• Enabled client to build refined sentiment models for social listening
• On-time delivery across 5-month execution period