Using Sentiment Analysis for Feedback

Sentiment analysis plays an important role in many industries. From market research to customer feedback analysis, sentiment analysis can be used to pull insights from text data. When put into action, sentiment analysis is the process of identifying opinions within data. While there is variance in methodology, sentiment analysis follows the same basic structure. After the necessary data preprocessing, sentiment analysis is run to score text fragments on a scale of positive, neutral, and negative.

Understanding How Sentiment Analysis Works

Natural Language Processing (NLP) is a field of work at the intersection of computer science, linguistics, and artificial intelligence. While the field is vast, the fundamental question of NLP is: how can we give computers the ability to understand written and spoken language the same way humans can? There are several NLP-based tools that you’ve probably encountered in your everyday life, from Google translate services to your Amazon Alexa or Google Home. Despite the differences in service, each of these tools strives to computerize language. 

Our New Sentiments Analysis Feature

The value of NLP tools for co:census and our users is clear– it is a fundamental tool used to analyze qualitative data. Specifically, co:census has recently launched our new Insights tab, which currently features NLP based sentiment analysis. Sentiment analysis is the process of automatically detecting and labeling opinions in text. 

Sentiments Analysis Helps Your Analyze Public Feedback

For co:census, this means that we are able to automatically detect opinions in survey responses, and label them on a five point scale (very negative, negative, neutral, positive, and very positive). As a platform built on the value of qualitative data, the new insights features provide our users with clarity on their constituents’ opinions and common trends within the responses. Our platform allows users to look at sentiment scores related to specific top trends and commonly mentioned phrases.

Not only can users see what constituents are saying, but also how they feel about it. Sentiment analysis aids in public feedback by extracting opinions for analysis from a much larger data set, streamlining the process of addressing areas of concern and understanding points of success. Learn more about the co:census platform and schedule a demo here.