Sentiment Analysis Tools & Applications
Do you have a realistic idea of what customers think of your brand? It can be hard to gauge sentiment by just speaking to customers in a store or over the phone. Even surveys may not uncover customers’ true feelings. But you need to know how satisfied your customers are with the products and services you provide to ensure your business is on track — or to make improvements that can regain their trust and loyalty.
In an increasingly digital world, customers have more soapboxes they can climb on to share their honest opinions. They’re posting on social media, weighing in on Yelp, writing blogs, and talking about your business in videos. Tracking down all of the content that mentions your business and analyzing feedback would be a monumental task to manage manually. Fortunately, you can leverage sentiment analysis tools to do the job faster and more thoroughly than you could on your own.
Types of Sentiment Analysis Tools
Sentiment analysis tools search content for indications of positive, neutral, or negative feedback about your business or your brand. You can also look for feedback on particular aspects of your products or services, such as the percentage of positive, neutral, or negative comments on pricing or reliability.
Fine-grained sentiment analysis is able to assess user content in a more detailed manner, expanding results beyond simply the percentage of very positive, positive, neutral, negative, or very negative. Sentiment analysis tools can also detect emotion in text or voice data.
How To Make a Sentiment Analysis Tool Work
Sentiment analysis uses different machine learning models to evaluate feedback and detect emotion.
Some models are rule-based. They compare survey responses or other content to an established data set that tells it whether the feedback is positive, neutral, or negative.
More advanced tools perform automated sentiment analysis. Automated sentiment analysis uses natural language processing (NLP), a type of artificial intelligence that enables computers to process and analyze language data. Training a model to understand sentiment conveyed in text or voice data is a complex task. Training data needs to take into account that a customer may make grammatical mistakes, use slang, or misspell words.
The data annotator labeling training data for sentiment analysis must have a deep understanding of the language and speech patterns that people use in real life, so the model will be able to differentiate between intent when a user says, for example, “I was shocked” in a positive way vs. a negative way.
Sentiment analysis tools designed to detect emotion also need training data that enables them to detect anger, disappointment, pleasure, excitement — and even sarcasm.
Some sentiment analysis tools have a hybrid design, using both rule-based and automatic, machine learning-based analysis.
Sentiment Analysis Applications
Depending on your business and the opinions you want to mine from customer content, you can use sentiment analysis in several ways:
- Social media monitoring: If your customers actively share information about your business on social media, you may want to focus your efforts on keeping up with the high volume of social posts, finding those that mention your business or products. Data annotators need to train these tools to understand the abbreviations, emojis, and hashtags commonly used in social media content.
- Voice of Customer (VoC): For a broader view of customer sentiment, you may choose a tool that mines opinions from multiple sources, including surveys, reviews, and blogs as well as social media, to determine customer satisfaction and provide you with insights to help you improve it.
- Voice of Employee (VoE): You may also find value in keeping a close watch on employee sentiment as well as customer sentiment. Not only can it help reduce the time and costs associated with employee turnover, but it can help you foster happier employees that can provide better customer service. McKinsey & Co. found that VoE applications can help improve productivity by 25 percent.
Making Sentiment Analysis Work for You
Sentiment analysis is useful to learn what customers think and feel about your business, but you can also use insights to manage your business more effectively. If you correlate feedback with other data, such as products purchased, promotional timeframe, or the team that provided service, you can pinpoint areas that may need improvement and identify areas where your business really shines. You can also compare sentiment before and after making changes to confirm you’re steering your business in the right direction.
If you’re wondering whether ideas you have for improvements to your business practices will impact customer sentiment, there’s no need to wonder. You can know.
Opinions expressed by Daivergent contributors are their own.