Sentiment Analysis
and Emotion AI​

Access over 10 million people around the globe to power your data. Use the standard positive/negative/neutral labels or even follow your own scale of emotion.

Sentiment Analysis

Sentiment analysis, also known as opinion mining, is an approach that employs natural language processing (NLP), computational linguistics, text analysis, and biometrics to detect the emotional tone behind specific text. ​

Where we help

Primarily Sentiment Analysis is used to make sense of unstructured and disorganized bodies of text in order to provide valuable information, usually around the sentiment of customers and users of a company’s products and services. This can be divided across multiple use cases.
 
In social media and brand monitoring, a company can set up alerts so that when they’re mentioned online, they can monitor the conversations and benefit from a commercial understanding of the customers’ sentiment in near-live time. This can assist with mitigation and enhance public relations. Accuracy is key, and we provide you access to such data at 10% higher accuracy levels than the leading market competitor.
 
Overall customer satisfaction can be enhanced greatly by using sentiment analysis. For instance, when it comes to tracking customer support tickets, managing reputation and conversations, analyzing human emotion in chatbot and voice assistance conversational systems, this is a critical space where companies can gain precious insights to better understand their customers and the market temperature.
In order to hit accurately and avoid potentially worsening the relationship, it is crucial that the sentiment is analyzed accurately. For this, a wide range of high-quality data is required, and speed is of the essence. We provide labeled data 50% more quickly than traditional methods.

Recently we’ve seen an increase in the number of solutions on the market for automating Human Resource (HR) processes. Here, interviews can be initiated by bots or avatars, saving hiring companies time on the initial stages of recruitment. Multiple severely problematic instances have been reported of such systems demonstrating biases, and many companies have even hit the headlines due to catastrophic mismanagement of such implementations.

High quality data from more diverse sources would help to mitigate such problems. Access over 10 million people from all walks of life based around the globe thanks to our unique methodology.

How we help

We can assist you to ensure accuracy and diversity in your emotion data 50% more quickly than typical methods. Use the standard positive/negative/neutral labels or even follow your own scale of emotion that we can easily implement.

Source your data from our diverse group of over 10 million people around the globe. Generate new data relevant to your project requirements or similar to your existing data examples, including speech and text, curated around the emotions you seek to replicate.
Gain 10% higher accuracy than the market-leading competitor for your diverse sentiment labeling tasks. Access fast and accurate labeling for your existing sentient datasets, such as images and invoices to be digitized, handwriting recognition, and other such cases. Benefit from 3-5 humans verifying each data point at no extra cost.

Serve multiple languages at once and ensure 100% compliance with the world’s strictest security protocol.

You'll be in a good company

We’ve completed standalone & end-to-end machine learning projects for both small and enterprise-size companies across the world.
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