Text Analytics
Emotion, sarcasm, and slang. These are just a few of the complexities of language that make text analytics so complicated. So when you ask a computer to understand and analyse speech—across multiple languages—it’s difficult to capture every nuance.
Identify Critical Issues in Real Time
From real-time anomaly detection and root cause analysis to data exploration and understanding trends, text analytics are at the core of everything we do—so you’re empowered to take the right action at the right time.
Finding the Perfect Balance
With text analytics, there is a balance between time, expense, and accuracy. Different models and methodologies will have varying impacts on your business, so it’s important to determine which approach best fits your priorities. We’ve built our text analytics engine to simply work from the day you turn it on—with intelligence that gets smarter over time—so you can feel confident when you take action.
Machine Learning Sentiment
Decipher and Decode Emotion, Sentiment, and Intent
When you understand the difference between a minor nuisance and a major issue that puts customer loyalty and revenue at risk, you can identify—and replicate—the types of experiences that elicit unbridled joy and increase customer retention and spend. Enter InMoment’s Adaptive Sentiment Engine.
Rules-Based Sentiment
- Based on rigid rules
- Requires manual intervention
- Accuracy never improves without continuous manual updates
Adaptive Sentiment Engine
- AI-driven—gets smarter over time
- Accuracy continually improves
- Ability to recognise new terms and phrases
Since switching from a rules-based model to InMoment’s Adaptive Sentiment Engine, we’ve seen a significant uptick in sentiment tagging accuracy.
Tyler Saxey, Director of Customer Experience, Footlocker
Our AI-driven Adaptive Sentiment Engine gets smarter and therefore more accurate over time, boasting the ability to correctly determine the sentiment of new terms and phrases as they enter the customer vocabulary—so you never miss a trend.
Native Language Text Analytics
Native Language When You Need It
In 99.9% of cases, machine translation for text analytics will get you the accuracy you need. However, some programmes may benefit from using native language libraries that help capture the sometimes critical cultural nuances that can be lost in translation. Fully featured native language text analytics help you accurately identify trends and opportunities in large amounts of data.
