The challenge
Determining whether customers intend to return to a product or a service based on their reviews requires sentiment and intent analysis.
Determining intent within review texts, and running intent analysis on streamed or batch processed data, can be a challenging task. It involves combining reviews from multiple sources and processing large volumes of data using NLP algorithms.
How to do it with Datastreamer
You can build this analysis into a single pipeline. Each step handles one part of the work, and the steps run together so review data is collected, standardized, and analyzed in one flow.
- Fetch content from multiple review sources. Create a powerful pipeline to fetch content from multiple review data sources simultaneously, so all of your reviews arrive in one place.
- Standardize the incoming data. Use the Unify Schema component to standardize incoming data from every source into a single, consistent structure.
- Add content analysis operations. Attach content analysis operations to the pipeline so each review is processed as it flows through.
- Apply sentiment and intent analysis. The AI Sentiment Classifier and Intent Classifier can be used together to identify positive and negative intent within review text.
Why it works
These operations work individually or together. Combining the Sentiment Classifier and the Intent Classifier lets you read whether customers intend to return to products or services based on what they write in their reviews, across every source at once.