Achieve an end-to-end view into your consumer preferences with our dynamic emotion tracking solutions. Detect consumer mood changes, prevent churns, and be the leader of proactive customer service.
Know exactly what your customers are up to with our user intent analytics solutions. Track and classify queries, comments, emails, and other data to reduce the time and costs of customer service.
Gain knowledge from unstructured content with and extract valuable information from all kinds of data. Make the most out of your emails, scripts, documents, and conversations – with less effort and quicker turnarounds.
Transform your business requirements into full-fledged smart solutions based on Natural Language Processing. Uncover insights buried in your data and transform opportunities into great products.
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Sentiment analysis as a service allows companies to look into the customer sentiment expressed through text or audio data. The technology enables individuals to identify, extract, quantify, and analyze subjective information as well as act on the subjective data to boost customer loyalty.
Businesses can improve consumer satisfaction by analyzing how well the products or services click with the user’s needs. Companies can also employ the technology to predict the success of a new product or service.
This type of data gives you an overall image of your customers’ emotions and opinions based on engaging with your brand at a specific moment. By getting a better handle on this data, you can measure customer satisfaction, identify pain points, and track opinions over time.
The technology is based on the latest advancements of machine learning and NLP techniques. Sentiment analysis as a service can also be divided into fine-grained, aspect-based, emotion, and intent-based analysis. The tech stack is different for each type.
The technology has been proven to be an effective tool for improving customer experience, driving sales and increasing revenue.
Some of the most popular use cases in marketing include:
There is no single best model for this technology. Instead, there are a variety of methods that can be used to build a sentiment analysis model with high accuracy. Some of the most popular methods for building sentiment analytics models include support vector machines, Naive Bayes classifiers, and recurrent neural networks. Our ML engineers select a custom set of technologies that fit your data specs.
