Sentiment Analysis

Understand the social sentiment of your brand, product or service while monitoring online conversations. Sentiment Analysis is contextual mining of text which identifies and extracts subjective information in source material.

Sentiment API works in fourteen different languages mentioned here.

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You might also be interested in these APIs: Emotion Analysis, Intent Analysis, Named Entity Recognition

Why our Sentiment Analysis API ?


Komprehend Sentiment Analysis API maintains high accuracy in real world, and is robust against tricky sentences like double negatives (“not bad”) and word order (“crushed my hopes” vs “crush on her”).


Process and return results in extremely short time, meeting demands from various industries.

Flexible Deployment

Komprehend Sentiment Analysis support private cloud deployments via Docker containers or on-premise deployment ensuring no data leakage.


Sentiment analysis API provides a very accurate analysis of the overall emotion of the text content incorporated from sources like Blogs, Articles, forums, consumer reviews, surveys, twitter etc. Sentiment Analysis can be widely applied to reviews and social media for a variety of applications, ranging from marketing to customer service.

It uses Long Short Term Memory (LSTM) algorithms to classify a text blob's sentiment into positive and negative. LSTMs model sentences as chain of forget-remember decisions based on context. It is trained on social media data and news data differently for handling casual and formal language. We also have trained this algorithm for various custom datasets for different clients.

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use cases

Improve Customer Satisfaction

Sentiment analysis quantifies the perception of current and potential customers on careful examination of their feedback. This helps you in streamlining the focus on customer satisfaction and building an appealing branding technique which directly improves your bottomline.

Reputation Management

Sentiment Analysis can monitor all the conversations around your brand in real-time and can help you prioritize those conversations having the most negative sentiment to protect your brand reputation . Komprehend's Sentiment Analysis is trained on millions of tweets and comments and therefore, works especially well to analyze user generated content.


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