With Qualaroo's integration with IBM Watson's Natural Language Understanding, go beyond the words and gain insights into the emotions the writer was feeling. Analyze freeform, text-based responses to identify sentiment, emotion, and relevant keywords associated with your qualitative data.
Finally, a scaleable way to swiftly and accurately categorize and assign values to your qualitative data. Categorize your text-based responses an overall positive (0 to 1) or negative (0 to -1) value.
Analyze your qualitative data through a quantitative rating of five emotions: Anger, Disgust, Sadness, Fear, and Joy. Each grouping of freeform content is assigned a value from 0 (low expression) to 1 (high expression) for each emotion.
Isolate relevant key words your customers are using to understand which areas of your product are exceeding, meeting, or failing to meet expectations.
How do I set it up?
You have the option to enable this feature whenever you have a free-text response selected. Simply select "Enable sentiment analysis"
Sentiment Analysis results are available through CSV export or by integrating with Qualaroo's API. We recommend using tools like Zapier or Webhooks to process your sentiment results in real time. Sentiment Analysis results will soon be available within your Qualaroo Dashboard. If you need help implementing these integrations or cannot see the option to "Enable sentiment analysis", let us know at firstname.lastname@example.org. We are more than happy to help!
Accessing Watson data through CSV export
Watson data is currently available through Version 2 of CSV exports. In order to select version two, navigate to the Reporting Dashboard by selecting View Responses.
Once you are in the Reporting Dashboard, select Export response and CSV V2 (BY QUESTIONS).
Accessing Watson data through the Reporting API
Example JSON output for both Reporting API and Webhooks
"question_title": "What is the reason for your answer?",
"answer": "Easy to implement, super useful, doesn't obstruct user experience in any significant way.",
"significant way": 0.1,
"user experience": 0.1
How can I use Sentiment Analysis?
The number of ways you can use Sentiment Analysis to learn and act upon your data are endless, but we've selected a few use cases to spark your creativity.
Sentiment Use Cases
1. Prompt your customers to provide open-text feedback on a product or service you offer, or simply about their experience with your organization. Use Qualaroo's IBM Watson integration to efficiently categorize this qualitative data into positive or negative sentiment to assist with your outreach initiatives. Organize your communication based on sentiment, and prioritize your efforts by focusing first on your respondents who provided negative sentiment.
2. Analyze the feedback corresponding to NPS ratings to predict future opinions and behavior. Determine which promoters provided open-text responses with negative sentiment, and reach out accordingly to maintain their status as a promoter. Conversely, use Sentiment Analysis to detect Detractors who might have provided a neutral statement and provide them extra attention in an effort to increase their opinion of your organization.
Emotion Analysis Use Case
Understand which pieces of feedback convey emotion most strongly. Consider a company who wants to increase the number of positive ratings they receive on a public forum. By analyzing the emotion of the feedback they receive on-site, that company can identify users who's feedback contains the most joy surrounding their offering and therefore more likely to promote their company on a public forum. They can then incentivize those users to leave public feedback and increase their social presence.
Key Words Use Case
Use Qualaroo's IBM Watson integration to understand which parts of your offering are successful or need attention. Consider a company who is gathering feedback to generate a Net Promoter Score. With Key Word analysis, determine which topics are associated with Promoters, Passives, and Detractors to understand where that company is doing well and where it needs to focus its attention.