The ability to qualify and quantify sentiment embedded in conversation is a growing field of interest across industries. When you can gauge the feelings your customers and agents have about your processes, you take an important step toward optimization.
Each conversation that happens in your call center is an opportunity for insight. But, how can you accurately analyze such vast amounts of data?
Let’s dive into what sentiment analysis is and how to do it.
What is Sentiment Analysis and How Do You Do It?
Seasoned veterans of conversation (humans) often have difficulty discerning the sentiment behind a statement. This means the accuracy of the data gained from conversations isn’t always the highest. If your data is inaccurate then the conclusions you draw from it are meaningless.
What’s more, manual processing of all this data would take a lifetime, literally.
Artificial intelligence, text analysis, machine learning, and natural language processing have come a long way in the past few years. These technologies have turned sentiment analysis into a precise way to determine the emotional tone of conversations. It is automatic and requires little input once it has been configured.
How Does It Work?
Sentiment analysis uses machine learning algorithms to automatically gauge conversations for their sentiment. Before we jump into algorithms, let’s consider the different systems a conversation can be analyzed by.
Sentiment analysis systems
There are three main systems for analyzing sentiment. Each system is infinitely refinable. You can apply any combination of these systems to the content you are analyzing for increased understanding.
Polarity is used to determine how closely sentiment falls on a scale of two extremes. The three basic points within this system are positive, negative, and neutral. You can make your scale more precise with more points in between the two extremes
The five-star system is a time-tested example of polarity.
You can draw emotional meaning from conversations by using lists that target and count words associated with certain emotions. In emotion detection, words are categorized by which emotion they are associated with. For example:
- Beautiful, fantastic, excellent, and great
- Disappointing, terrible, bad, broken
- Fine, undecided, color words
When analyzing conversations for emotional meaning, algorithms can be trained to associate words with categories. They then can classify the overall emotional tone.
Aspect-based sentiment analysis lets you zero in on where you need to take action. It lets you sort out which part of a conversation is positive, negative, or neutral. Reviews, news articles, or social media posts can be broken into digestible and meaningful pieces using an aspect-based system.
A survey might ask, “please rate your workspace.” In response, an agent might write, “There are too many tabs open on my desktop and not enough time to find the information I need.”
Aspect-based sentiment analysis allows you to infer that time and desktops are an issue in conjunction so you can focus on finding the right solution for that agent.
Sentiment analysis tools
The tools you employ to analyze sentiment are different kinds of algorithms. They are rule-based and automatic.
The result of these processes is a sentiment score. These scores give you insight into the emotion behind what your customers and agents say and write.
These algorithms use rules manually input by humans to identify sentiment within a conversation and rely on NLP techniques. You can tell your sentiment analysis program what words to pay attention to and what sentiment they implement. You create a list your rule-based algorithm uses to assign meaning to conversations and text.
Rule-based algorithms might use text analytics to detect positive, negative, or neutral words in a piece of writing. If there are more positive words than negative or neutral, the piece of writing is given a positive sentiment score.
However, rule-based algorithms work within the set of rules you input and only those rules. They have trouble accounting for context and require fine-tuning. They work best for basic sentiment analysis.
Automatic systems leverage machine learning to carry out analysis for sentiment. These systems require being fed training data to get the learning directed on the right path.
To begin, the input text is run through a feature extractor to get a feature vector. These feature vectors are then paired with tags (positive, neutral, or negative). This creates a model your automatic algorithm can now use for sentiment analysis.
Because automatic algorithms use machine learning, they can learn from their experiences and expand their lists of feature vectors/tag associations. They fine-tune themselves.
Applications of Sentiment Analysis
Sentiment analysis can be used on both sides of the call center conversation. This holistic approach gives you a better reading as to how you can create processes that best serve your organization.
Customer sentiment analysis
Customer sentiment analysis gives you insight into what your customers feel toward your organization. It can also help you determine what your customers need. This is a key part of understanding how you can best serve your customer.
You can gauge how your customers feel about your organization by monitoring their sentiment. If you start to notice negative feedback you can change processes and behavior to counter negative mentions and improve them.
Agent sentiment analysis
Analyzing agent sentiment can tell you a lot about the health of your customer interactions as well as job satisfaction.
When your agents are engaged, they are more likely to stick around. Agent retention builds consistency and a better customer experience. Sentiment analysis tools help you pinpoint what does or does not keep your agents engaged.
Sentiment analysis also gives you the opportunity for tailored training. Customer interactions become more healthy when agents’ abilities and skills grow to meet your customers’ needs.
Analysis of a conversation might show an agent lacks confidence when talking about a specific subject. With this knowledge, your management can engage in specific training that benefits both agent and customer alike.
One Step Ahead
Sentiment analysis is often overlooked as a tool that lacks the precise ability to read emotions in human language. But machine learning, AI capabilities, and natural language processing technologies are improving. Sentiment analysis can leverage this growth.
Algorithms based on machine learning automate and increase the accuracy of sentiment analysis so you can stay one step ahead of your customers’ expectations while also keeping agents happy.