What is Sentiment Monitoring?
For businesses to be successful, owners should have an understanding of how customers feel about their services and products. Positive or negative feelings matter and can either boost business operations or lead to a crisis. Sentiment monitoring or sentiment analysis offers a way to measure customer satisfaction.
With the rise in digital technology and E-Commerce, sentiment monitoring has gained a lot of popularity and become a vital part of businesses. In the blog, we will learn about what is sentiment monitoring, how does it work, how to use it, and other things related to it.
Sentiment Monitoring – What Is It?
Sentiment analysis or sentiment monitoring is a method used to analyze the opinions of groups or individuals. These include a part of a brand’s overall audience or an individual who communicates with customer support officials. Sentiment analysis uses a scoring mechanism to analyze and evaluate conversations, voice inflections, language, etc., to quantify people’s emotions, opinions, and feelings towards the brand’s services or products.
Sentiment monitoring is also known as opinion mining. Being part of natural language processing and the speech analytics system, it is a vital component used to determine customer attitudes and opinions.
How Does Sentiment Monitoring Work?
Usually, sentiment monitoring works on an algorithm that scores the words spoken by a customer and voice inflections that showcase the person’s feelings and opinions about the concerned topic. This process allows objective interpretation of subjective and hard-to-measure factors like-
- Rate of speech or how fast a customer is speaking
- Changes in stress levels showcased by the customer’s speech. These changes are usually in response to solutions offered by customer support officials
- The amount of frustration or stress in a person’s voice
In most call center and customer service operations, sentiment monitoring plays a vital role in monitoring the feelings, emotions, and opinions of a wide range of customers. It helps when customers are talking to support officials, customers talking during shifts, customers reporting specific issues, customers talking about certain services or products, and similar scenarios.
Sentiment monitoring can either be totally human-based, fully automated, or a combination of both these. Usually, sentiment monitoring is primarily automatic with some human oversight that enhances machine learning and refines other algorithms, mainly during early implementation.
Understanding Sentiment Score
One of the ways to measure sentiment monitoring is through the sentiment score. This scaling system reflects the depth of emotions in a text. It is vital to understand how a customer feels, and the score ranges from 0 to 10, from negative to positive sentiment.
Although there are many ways to calculate sentiment score, the best way is through a dictionary of neutral, negative, and positive words. A content or text is monitored to check the number of positive or negative words it has, which gives an overall idea of a text’s sentiments and feelings.
Many factors affect the sentiment score, including a number of types of emotions, strength of emotions, and their context. It is one of the best ways to determine customer satisfaction.
What is Sentiment Analysis Used For?
These are the main things for which brands use sentiment monitoring-
Monitoring brand health – Sentiment monitoring helps assess brand health. Brands can see the ratio of negative and positive mentions about their brand on social media and other platforms. They can use this feedback to know what customers like and what they complain about.
Track campaign performance – Brands can track positive or negative mention of their marketing campaign, events, and other collaborations on social media, tweets, etc. Measuring overall sentiment around marketing campaigns is important, and brands can identify positive strategies through this.
Improve customer support – Sentiment monitoring tells brands about how customers feel about their products and services. Negative mentions push brands to work on these issues and provide a better experience to their customers.
Compare with competitors’ analysis – A brand can get positive, negative, and neutral mentions, but they are of no use if you cannot compare them with a benchmark. Brands can monitor customer sentiments of their competitors and have an idea of where they stand.
Catch reputation crises – When brands spot a rise in negative mentions, they can look into the matter right away and prevent a reputation crisis from taking place.
How to do Customer Sentiment Monitoring?
Sentiment monitoring is either human-based, automatic, or a combination of both. Natural Language Processing is used to classify sentiments through these models-
Supervised Machine Learning
In this model, a system is introduced to sets of labeled data whose sentiments have already been classified by humans. The system learns from this dataset and then labels new data itself. Machine learning offers automated text analysis for sentiment monitoring.
This system involves human-crafted text analysis rules. These rules usually have pre-labeled expressions and words. This model is not resource-heavy, but it analyses individual phrases and words instead of a sequence.
These models include a combination of machine learning and rule-based methods. Usually, the rule-based system tries to classify the sentiments of a statement or content first. If this does not work as intended, machine learning classification is then used.
Practice Sentiment Monitoring to Identify Positive and Negative Sentiment of Customers
Sentiment monitoring helps brands understand texts on an emotional level and classify negative, positive, and neutral sentiments. Brands can use the technique to understand their customers’ opinions and feelings regarding their services and products.
Understanding feedback and reviews help brands improve their services and offer better satisfaction to their customers. Sentiment monitoring sounds difficult, but it is actually a straightforward and effective technique.
There are multiple online tools available that can help brands start with sentiment monitoring. We recommend all brands to use this technique and adjust their operations based on their customers’ feelings and sentiments.