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Sentiment Router

Sentiment Analysis for the contact center

Route social interactions based on sentiment

Assign incoming social interactions to the most suitable agent based on the sentiment of the interaction. The sentiment router can automatically decipher whether customers comments are positive, negative or neutral. It can also determine possible sale opportunities by steering comments that contain questions.

Sentiment-Based Routing (SBR)

Arabic NLP Skill-Based Routing

API Integration

10 Langauges

Set your own routing rules

Priority routing

Assign negative comments higher priority to resolve issues in real-time.

Skill-based routing

Route comments to the most relevant skill groups based on the topic detected.

Enquire About Sentiment Router

Dig into the core engine

01 - Arabic NLP

Built using NLP techniques to provide high accuracy Arabic sentiment analysis and topic detection.

02 - Machine Learning

The solution accumulates data continuously enhances accuracy via self-learning. Therefore the model will automatically.

Context Awareness

Sentiment is measured taking context into account. Comments containing humour, sarcasm and/or use of metaphors will be measured accordingly.

04 - Industry-Focused Algorithms

Banking, Telecom, Government & Healthcare.

Automated Sentiment Analysis

Listen your customers sentiment and act quickly. IST’s Sentiment Router is specifically built for Cisco and Genesys Contact Centers to allow your contact
center agents to react to customer sentiment, in an ordered and prioritized manner.

The Sentiment Router automatically deciphers incoming social interactions
and comments into positive, negative or neutral categories, which can then
be routed to specifically trained agents for action. Furthermore, interactions
that represent a sales opportunity are steered accordingly to your sales team.

Expert Arab Linguists

The Arabic language has very difficult and unique grammatical rules. Our highly skilled team of Arabic linguists has solved many of the challenging issues faced
with Arabic Sentiment. As a result, IST’s Sentiment Router provides extremely accurate processing of Arabic Sentiment so all social interactions are routed to
the right agent at the right time.

Engine Features

Feature Description
English & Arabic
Sentiment Analysis
All interactions are categorised into
positive, negative, neutral based on the
‘sentiment’ or emotion expressed in
the comment. Highly accurate Arabic
Sentiment analysis based on natural
language processing (NLP).
Aspect
Detection
Topics or “Aspects” discussed within
comments are detected. Aspects are
pre-defined words that are selected
based on industry relevance. E.g., in
banking, aspects could include: Loans
and Credit Cards.
Question
Detection
Comments containing questions are
detected and categorized as questions.
Context
Recognition
Sentiments are accurately filtered and
measured with context consideration.
Comments containing sarcasm, humour
or use of metaphors and expressions will
be detected and measured accordingly.
Optimised for
Cisco & Genesys
The engine can be integrated with Cisco
and Genesys contact centers within 60
minutes. Making the integration process
a five-finger exercise.
AI & Machine
Learning
Data is accumulated and accuracy is
continuously improved via self-learning,
to include additional vocabulary, phrases
and slang.
Improved CX


Agents can more quickly attend to customer needs, thanks to the power of actionable intelligence; customers are efficiently routed to the most appropriate agent that can resolve their issue. With timely resolution of their issues, customers will no doubt be more satisfied with the overall contact experience.

Increased Net Promoter Score (NPS)


By addressing your customer’s issues efficiently you’re turning dissatisfied customers (detractors) into satisfied customers (promoters) raising the chances of them recommending you to others.

Optimized for Cisco & Genesys contact centers


Especially built for Cisco & Genesys The Engine can be integrated with Cisco and Genesys contact centers within 60 minutes.