Sentiment analysis is a technique for assessing the views of people or groups, such as a section of a brand’s audience or a single client in contact with a customer service agent. Using a scoring method, sentiment analysis observes conversations and assesses language and speech inflections to measure attitudes, views, and feelings about a business, service or product, or situation. Sentiment analysis is also described as opinion mining. It is an integral aspect of the whole speech analytics system as it reveals a customer’s opinions or attitudes.
How Sentiment Analysis Works
In sentiment analysis, the words used, as well as voice inflections, are often scored by an algorithm, which can reflect a person’s underlying thoughts regarding the topic of a conversation. Sentiment analysis enables a more objective interpretation of variables that are difficult to gauge or are generally measured subjectively, such as:
- The rate at which the person speaks (rate of speech)
- Changes in the person’s stress level as evidenced by their speech (such as in response to a solution provided by a customer support representative)
- The tone of a customer’s voice when he or she is stressed or frustrated.
In customer care and call center applications, sentiment analysis is a useful tool for evaluating opinions and attitudes among diverse customer segments, like customers talking with a specific group of representatives, during shifts, clients phoning about a specific issue, product, or service lines, and other different groupings.
Sentiment analysis can be completely automated, totally based on human analysis, or a hybrid of the two. In certain circumstances, sentiment analysis is largely automated with some degree of human oversight, which drives machine learning and aids in the refinement of algorithms and processes, especially in the early stages of deployment.
Sentiment Analysis Examples
Sentiment analysis is utilized in a wide range of applications and for a variety of reasons. On Twitter, for example, sentiment analysis can be used to estimate overall opinion on a hot topic. Sentiment analysis is frequently used by businesses and brands to track brand reputation across social media platforms and the internet as a whole.
One of the most extensively utilized applications for sentiment analysis is for tracking call center and omnichannel customer service performance. Sentiment analysis is rapidly being used for general brand monitoring as organizations want to keep a finger on the pulse of their audiences.
Political candidates and administrations have utilized sentiment analysis to track public opinion on policy reforms and campaign releases, allowing them to perfect their strategy and messaging to better connect with the electorate and constituents.
In brand reputation management programs, general trends in sentiment analysis help brands to discover highs and lows in overall brand sentiment or variations in views about products or services, thereby allowing companies to make modifications that are exactly in sync with customer expectations.
4 Use Cases for Sentiment Analysis
Using Opinions to Segment Buyer Groups:
The ability to track consumer sentiment allows a company to understand which customers are more outspoken than others. For example, many people speculate that 20% of customers cause 80% of customer problems. If this statistic is correct, you will be able to segregate the traits of that group and either address their prevalent problems or even avoid the group altogether. (Of course, removing buyers would imply that, based on the level/type of opinions of that group, there is little to no ROI).
This might inspire you to update software, improve the design of tangible goods, or improve your services. This information can sometimes lead to new products or services for your company to provide.
Plan Improvements to the Process:
Customer feedback isn’t always favorable. Negative feedback, on the other hand, isn’t always untrue. These viewpoints may require systematic categorization, implying an improvement of your overall customer service (or any other) procedure.
Track Sentiment Continuously Over Time:
The sentiment is a parameter that should be checked on a regular basis. Opinions will evolve as you enhance your processes and products. Seeing these shift patterns allows you to better navigate the emotional waters of sentiment.
Sentiment Analysis Tips & Best Practices
Language is complex in nature, and sentiment analysis, as a method for quantifying and assessing it, is similarly complex. What is comparatively easy for humans to judge subjectively in face-to-face interaction, like whether someone is happy or sad, thrilled or upset about a topic, must be interpreted into objective, measurable scores that account for the several intricacies that exist in human language, especially in the context of a conversation.
When used with the appropriate technology tools and applied to important business drivers, sentiment analysis is an effective tool for directing organizations and their specific business units to desirable results from every customer engagement and business performance improvement.
Understand how to categorize sentiment according to the various approaches
- Machine Learning: In this approach, a classifier is built using a machine-learning algorithm and a variety of features to detect text that indicates sentiment. Deep-learning approaches are popular nowadays because they are compatible with data learning representations.
- Lexicon-Based: This method determines the overall assessment score of particular content by examining a set of words marked with polarity scores. The technique’s biggest feature is that it doesn’t require any training data, but its biggest shortcoming is that sentiment lexicons exclude a vast number of words and expressions.
- Hybrid: Hybrid is a term that refers to a combination of machine learning and lexicon-based techniques to sentiment analysis. Despite the fact that it is not widely employed, this strategy usually yields more promising outcomes than the methods discussed above.
Unlike some examples, make sure your methods are ethical
With the advancement of technology, sentiment analysis is emerging as a more widely used tool for enterprises. It is used by social media monitoring programs to provide customers with information about how the public feels regarding their brand, products, or areas of interest.
It’s extensively utilized by email services to keep spam out of your inbox, as well as by review websites to propose fresh content such as movies or TV series.
However, it has been utilized in more turbulent situations. For example, when it was reported that Facebook was utilizing sentiment analysis to explore if they could control people’s emotions by tweaking their algorithms to infuse negative or positive content more often into their users’ news feeds, the company came under attack.
They discovered that by bombarding their users’ news feeds with positive or negative content, they could significantly impact their emotional output by exploiting this mechanism of “emotional contagion.” The main issue is that Facebook did not disclose to its users that they were participating in an experiment, which may have caused them mental discomfort in some situations.
We can now understand how this use of sentiment analysis could be morally wrong in the long run.
Examine the sentiments of your competitors as well as your own
In addition to tracking your own online remarks, you can also follow the mentions of your competitors to evaluate how your company compares. Positive attitudes can assist you in determining where your competitors are thriving.
Negative attitudes can disclose new business opportunities for your company. A sudden burst of unfavorable reactions to a competitor’s product redesign could, for example, suggest an opportunity for your business to fill a gap.
According to Milosz Krasinski, “Following your competitors’ mentions can also help you identify areas where you can improve. If a competitor’s marketing campaign is getting higher marks than yours, examine it in detail and identify what tactics are most effective. Use these insights as inspiration for your next campaign.”
Sentiment analysis is a very useful tool for businesses since it allows you to receive honest feedback from your customers in an unbiased (or less prejudiced) manner. When done correctly, it may offer a lot of value to your systems, applications, and web projects. There is software on the market to help get you started. In addition, the majority of media monitoring software can perform this type of analysis.