Use case
Sentiment analysis
Identify the emotional tone and opinion expressed in GenAI model responses or simple text data for greater insights and holistic, context-aware responses
Why Labelbox for sentiment analysis
Generate high-quality datasets
Use advanced tooling, on-demand experts, AI, and real-time quality metrics to generate high-quality data for sentiment analysis.
Accelerate time to value
Use AI-assisted labeling, advanced tooling, and customized workflows to create quality training data for sentiment analysis.
Access on-demand sentiment analysis expertise
Embrace on-demand, highly-skilled labeling services and industry-specific insights that can scale with your business needs.
Collaborate in real-time
Enjoy direct access to internal and external labelers with real-time feedback on sentiment analysis tasks via the Labelbox platform.
Understanding sentiment analysis
Sentiment analysis provides insights into text data and allows models to learn how language correlates to situational context and emotional expression, serving as the foundation for GenAI tasks such as conversational AI, text generation, and more.
Challenges in sentiment analysis tasks
Emotions are often nuanced and dynamic, and can change throughout an interaction. This ambiguity makes it difficult to build accurate, fair, and effective models without a properly labeled set of text data to support critical post-training operations.
Sentiment analysis tasks with Labelbox
Labelbox empowers AI teams to train models that think, plan, and act intelligently. Our platform's flexibility and powerful annotation tools allow you to create diverse, tailored datasets that teach AI to understand natural language and adapt to changing emotional expression and context.
Customer spotlight
Labelbox's intuitive tooling coupled with post-training labeling services offered a collaborative environment where Speak's internal team, along with external data annotators, could work together seamlessly. Learn more about how Speak uses Labelbox to improving the quality and efficiency of their data labeling.
