Sentiment Analysis
Business success highly depends on its customer satisfaction and loyalty. It’s very important to closely monitor the opinions of your clients to know what they really think about your business and timely improve your products, services, customer support or else if needed.
Customer Sentiment Analysis
What is Sentiment Analysis?
Sentiment Analysis is a practice of collecting opinions of your customers about your business, its products or services. If done properly, sentiment analysis can reveal gold mines inside the thoughts and opinions of your customers. Sentiment Analysis uses NLP (Natural Language Processing), a Machine Learning technique, to analyze the language and the tone they use when they talk about your business (positive, negative or neutral way).
Where does the data come from?
Sentiment Analysis can be applied to any text data to understand the “affect” of the written language and can analyze millions of written reviews within minutes. The data can be collected from any source where your customers share their opinions on your brand, for example, social media comments, email database, or from other resources such as online reviews websites.
How does Sentiment Analysis work?
First we need to collect and clean the data and select attributes or keywords that will help us evaluate or assess the score of a text based on the attitude that usually goes between +1 (totally positive) and -1 (totally negative). Then we need to train the ML model to distinguish between an enthusiastic comment and a milder, still positive one and link it to the corresponding score correctly.
Understanding the results
At the end of the process, we should see the data grouped into major categories. We should see if we have more positive, neutral or negative reactions. It is especially important that each mood (sentiment) is tagged with its original date, as the timeline will tell us if we have had "peaks" (spikes in positive sentiment) or "valleys" (spikes in negative sentiments) at certain moments. In this way, we might find a correlation between what happened on a particular date and the surge in opinions regarding our brand.