Watson APIs

Announcing new service changes and upgrades to Watson Tone Analyzer

Share this post:

Key Points:
– We’re excited to announce new changes to the Watson Tone Analyzer service that reflect user feedback
– Three key changes include removing social tones, combining anger and disgust tones and outputting only dominant tones for text
– These changes are active as of 25 September 2017

Learn about Watson Tone Analyzer

 

We are pleased to announce new updates and feature additions to our IBM Watson Tone Analyzer service.

What you told us

Since our initial release last year, we collected feedback from users. We’ve used the feedback to inform the changes to the Watson Tone Analyzer service. You told us the following:

  • Some tones are more frequently used than others
  • Tones that are differentiated and actionable are most useful
  • Some users were not using the scores from the service appropriately due to confusion. For example, some used tone signals that scored very low or were weak to make important decisions as part of their applications

Changes we made and why we made them

To improve your experience and ability to make the best decision with the help of Watson, we’ve made the following updates:

Change: Social tones were removed from the service output. They consist of the Big Five personality dimensions: extraversion, agreeableness, openness, conscientiousness and emotional range.

Rationale: These tones were not being used. Removing them from the service simplifies and amplifies the experience.

Change: We combined anger and disgust emotional tones.

Rationale: We found our users had perception issue while differentiating anger from disgust. We believe this combined tone ‘anger’ would support the same use cases what would have been supported by anger and disgust as separate tones.

Change: We output only the dominant tones for a given text. Dominant tones are those that have scores of at least 0.5.

Rationale: In our earlier API version, we returned scores for all tones. However, we found that users were making incorrect interpretations of the scores and using weak tone signals (e.g., a score of 0.2 for anger) in their applications. That often led undesirable outcome in the applications. In this version, we want to ensure that only strong/dominant tone signals (i.e. score of at least 0.5) are given as output from the service. This eliminates confusions regarding tone scores and incorrect use of the tones. This is also consistent with our customer engagement endpoint of tone analyzer where we only return dominant tones (https://www.ibm.com/blogs/watson/2017/07/ibm-expands-watson-tone-sensitivity-for-customer-service/).

These changes will be available in our posted API beginning September 25, 2017. We are also excited to announce support of French in the tone API. You can send French text to Tone API for analysis by setting the content-language to fr.

For more details about Tone Analyzer Service, the science behind it, how to use the APIs, and example applications explore the documentation for Tone Analyzer.

 

Explore our Watson Tone Analyzer documentation and new features.

 

 

 

More Watson APIs stories

AIconics names IBM Watson Discovery Best Innovator in Natural Language Processing

June 20, 2019 | AI for the Enterprise, Discovery and Exploration

On June 11, the world’s only independently judged enterprise AI awards – the AIconics – named Watson Discovery the winner for “Best Innovation in NLP.” Natural Language Processing is the area of computer science and AI that governs the interaction between computers and human languages. Specifically, NLP concerns how computers process and analyze unstructured natural language data. ...read more


IBM Watson Assistant gets smarter and faster, making customer service a breeze

June 20, 2019 | AI for the Enterprise, Conversational Services

We're excited to announce new Watson Assistant features that are designed to change the way businesses interact with their users. Watson Assistant not only helps answer customer questions quickly and accurately, but it also ensures that employees are empowered to do their jobs efficiently. ...read more


Balancing personalization with brand consistency: Podcast interview with Tameka Vasquez & Oliver Christie

April 30, 2019 | AI for the Enterprise, Think Leaders

In this episode of thinkPod, we are joined by Tameka Vasquez (marketing strategist and professor) and Oliver Christie (futurist and founder of Foxy Machine). We talk to Tameka and Oliver about creating customer experiences that resonate, the beauty of simplicity and being jargon-free, and whether or not AI will replace human creativity with marketing. We also tackle whether marketers have been tone deaf and the difficulties of hyper personalization. ...read more