Customer Sentiment with

Text Analytics

Extracting meaning and emotion from text data at scale

Companies gain competitive advantage by using sentiment analysis of customer reviews and social media comments to drive the insights on how customers feel about the product or service offering. Sentiment analysis is the process of detecting positive or negative sentiment in text and helps businesses quickly understand the overall opinions of their customers. This understanding can then be translated into features that the team can implement to ensure they can delight customers.

It’s estimated that
of the world’s data is


Every day huge volumes of unstructured business data are created through emails, support tickets, chats, social media conversations, surveys, articles, documents, etc.

Use Cases

Voice of the Customer

Companies can use customer feedback and reviews to learn what makes customers happy or frustrated, so that they can tailor products and services to meet their customer's needs.

Disease classification

Meaning can be extracted from doctor’s notes and leverage it to enhance disease classification.

Manufacturing or Warranty Analysis

Companies examine the text that comes from warranty claims, dealer technician lines, report orders, customer relations text, and other potential information to extract certain entities or concepts (like the engine or a certain part). They can then analyze this information, looking at how the entities cluster and to see if the clusters are increasing in size and whether they are a cause for concern, for example.

Brand monitoring

Sentiment analysis of brands on social media outlets enables companies to keep current with their credibility within the industry, identify emerging or potential reputational crises, to quickly respond to them.

How it’s done

The large volumes of unstructured text data such as tweets, articles, reviews and comments are transferred into meaningful data for analyzing the pattern, trend and getting insights using natural language processing, text analysis and statistics to analyze customer sentiment.

Sentiment analysis algorithms fall into one of three buckets:


These systems automatically perform sentiment analysis based on a set of manually crafted rules. These rules may include various NLP techniques developed in computational
linguistics, such as:Stemming, tokenization, part-of-speech tagging and parsing.


Systems rely on machine learning techniques to learn from data. Sentiment analysis is usually performed as a classification problem, where texts are fed into a model to return the category as an output (e.g, positive, negative, neutral).


Systems combine both
rule based and automatic