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What customers really think—how sentiment analysis can help

What is sentiment analysis, and what’s it useful for?

Sentiment analysis involves analysing text, normally online, to assess customer opinion. It uses Natural Language Processing (NLP) and machine learning to analyse extracts of text, categorising them as positive, negative or neutral.

Also known as opinion mining, sentiment analysis helps you keep tabs on what customers think. It’s useful for monitoring your brand reputation and qualifying customer reviews. You can also use it to assess and categorise customer service requests, organising by priority, urgency or type of help required.

You can use sentiment analysis to analyse data from several different sources, such as social media, review sites, your own website or CRM system and survey responses. In fact, here at SurveyMonkey we use sentiment analysis to evaluate the responses to your open-ended questions.

People showing positive, neutral and negative sentiments.

Types of sentiment analysis

There are three main types of sentiment analysis. Which type you use depends on the level of detail you want to go into.

1. Fine-grained

This takes the broad categories of positive, negative and neutral and breaks them down further into: very positive, positive, neutral, negative and very negative.

2. Emotion detection

This type of analysis evaluates text to determine whether it reflects various emotions, such as happiness, sadness and frustration. To do this, it uses lexicons, or lists of words that have been categorised according to the emotion they express—for instance, “thrilled” to indicate happiness, “disappointed” to indicate sadness.

3.  Aspect-based

Aspect-based sentiment analysis involves determining not just what a customer feels about a product or service, but what exactly they are happy or unhappy about. For instance, in reviews for a gym, a customer might be satisfied with the equipment, but unhappy with the cleanliness or class booking system, and it’s useful to have that information.

How does sentiment analysis work?

Sentiment analysis is just one type of NLP, where artificial intelligence algorithms are used to extract meaning from human language. Search engines and digital assistant tools like Alexa and Siri use the same technology.

Sentiment analysis normally involves these steps:

  1. The software determines if a sentence is subjective or objective (objective sentences are generally defined as neutral).
  2. It determines if a sentence has sentiment, and if so, whether this is positive, neutral or negative, using one of these approaches:

Rule-based: using manually created rules and lexicons (word lists), this approach compares the number of negative and positive words to determine the overall sentiment.

Automatic: using machine learning, classifiers are trained on sample texts and tag sentences as positive, negative or neutral.

Hybrid: using a combination of the best bits of rule-based and automatic approaches.

3. You’re up next. Delve deeper into the results, for instance filtering responses by location, or filtering responses by emotion to identify trends and draw conclusions.

Where sentiment analysis struggles

As you can see, sentiment analysis is a powerful tool for understanding what people are thinking. But it’s not without its challenges.

These challenges stem from the complexity of language and human expression. We express ourselves in so many different ways, using different language and slang. And it’s constantly changing. This makes rule-based analysis, used for emotion detection, incredibly difficult, as words need to be constantly added or recategorised. Rhetorical devices such as sarcasm and irony, as well as implicit meaning, can also stump sophisticated programs, meaning text is sometimes misclassified.

And that’s before we even start thinking about taking sentiment analysis across borders, with different languages, dialects, cultural specificities and unique forms of expression. It’s clear that multiple markets and languages can pose a problem for sentiment analysis.

Examples of sentiment analysis in action

Using customer reviews to inform product development, positioning and customer service provision

Let’s imagine you work in the marketing team for a sportswear brand. You could extract product review data from your own website and from third-party vendor sites then run it through hybrid sentiment analysis. This will allow you to:

  • assess the number of overall positive, negative and neutral reviews
  • filter reviews by product to determine the most popular items and identify potential issues
  • use aspect-based analysis to understand what is and isn’t working with a specific product or range—is the quality not up to scratch? Are there problems with delivery? Or maybe the fit is off?
  • use these findings to inform your positioning, product development and customer service provision, to name just a few. For instance, maybe your customer support team needs additional training? Or there’s a design flaw with a particular product? Or perhaps your price point isn’t quite right?

Monitoring your brand reputation to identify issues and opportunities

You can use sentiment analysis as a PR or brand reputation tool by analysing online mentions of your organisation across a range of media. With effective brand monitoring, you can stay up to date with the conversations your customers, investors and competitors are having about you. This allows you to detect and respond to unhappy customers before things escalate, and also to capitalise on what you’re doing well, and even improve your brand reputation.

For instance, let’s consider the incredibly influential Elon Musk. In recent times, his comments on Twitter have seen Samsung Publishing share prices soar, and Bitcoin plummet. Being aware of what people are saying about your company as it happens means you can react or respond quickly to mitigate any risks.

Gaining greater insight into employee satisfaction

Gain greater insight into the state of staff satisfaction or engagement by using sentiment analysis in your surveys. This involves mining opinions expressed in open-ended questions and can help you understand:

  • the percentage of employees that are happy, frustrated, satisfied or other
  • how your staff feel about the prospect of coming back to the office
  • how many of your staff are likely to move on soon

Analysing open-ended questions can also help you put other answers in context. For instance, of those staff that say they are actively looking for other opportunities, how many are anxious or unhappy about returning to the office? Maybe this is a blocker for them.

The best sentiment analysis tools to get you started

Brandwatch, for brand reputation monitoring

Brandwatch is a great tool for keeping tabs on what people are saying about your brand or organisation. You can also check what they’re saying about your competition and stay up to date on the latest trends.

SurveyMonkey, for open-ended survey questions

Whatever type of survey you’re running—be it customer satisfaction, market research, brand awareness, or something else entirely—SurveyMonkey’s Sentiment Analysis tool allows you to categorise your open-ended questions. It sorts them into positive, negative and neutral. You can also assign relevant tags to responses and then filter based on tags. Plus you can filter overall results and individual questions by sentiment.

HubSpot, for analysing customer feedback

HubSpot’s customer feedback tool organises reviews based on sentiment, displaying them on a dashboard that gives a great overview of customer satisfaction.

Critical Mention, for monitoring and analysing news stories

Critical Mention monitors and analyses broadcast and digital media, including TV, radio and podcasts for brand mentions. 

Social Mention, for social media analysis

Social Mention’s free tool allows you to analyse sentiment for a particular search term or keyword.

MonkeyLearn, for web-wide sentiment analysis

MonkeyLearn provides a free tool that you can use for real-time monitoring of digital content about your brand, product or service.

Rosette, for analysing across 30+ languages

If your customers are spread across the globe, then Rosette is probably your best bet. Its sentiment analysis tool works across over 30 different languages, without the text needing to be translated first.

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