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.
Try our free demo now by typing a sentence or choose from the options in the drop-down menu.
Positive
Neutral
Negative
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.
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.
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.
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.
