A Guide to Text and Sentiment Analysis

Well, with text and sentiment analysis technology, you can easily quantify your customer’s feedback from online reviews, surveys or social media posts. The below guide will define both text analytics and sentiment analysis and how they give key insight into a company’s CX strategy.

Sentiment analysis defined

Sentiment analysis is a language processing technique that will assign a weighted “sentiment” score to elements of text from a customer. The text is then graded as positive, negative or neutral.

Using sentiment analysis, businesses can build their voice of the customer program. Companies can also gain insight into how customers generally view them. They’ll be able to pinpoint which specific interactions are influencing customer sentiment and therefore customer satisfaction.

Text analytics

Text analytics is the machine learning component of sentiment analysis. It’s the process or technology used to turn qualitative feedback into quantitative data. This data can then be measured for statistical analysis. Text analytics can find patterns in sentences which then gets processed to become sentiment analysis.

Both sentiment analysis and text analytics together give key insight that companies otherwise wouldn’t be able to obtain.

For example, HelloFresh used sentiment analysis to better understand their consumers’ dislikes, preferences, and needs. HelloFresh discovered that children disliked their casserole recipes because they couldn’t see or easily identify what was in their food. HelloFresh then modified their recipes to be more transparent about the ingredients within their casserole dishes.

In summary, sentiment analysis and text analytics are powerful tools that allow businesses to gain key perspicacity into how their customers feel and view a specific product, service or the company in general. By having this insight companies can make more informed and strategic marketing, CX and business decisions.

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