What is Sentiment Analysis? Examples, Best Practices, & More

A Definition of Sentiment Analysis

Sentiment analysis is a method for gauging opinions of individuals or groups, such as a segment of a brand’s audience or an individual customer in communication with a customer support representative. Based on a scoring mechanism, sentiment analysis monitors conversations and evaluates language and voice inflections to quantify attitudes, opinions, and emotions related to a business, product or service, or topic. Sentiment analysis is sometimes also referred to as opinion mining. As part of the overall speech analytics system, sentiment analysis is the integral component that determines a customer’s opinions or attitudes.

How Sentiment Analysis Works

Sentiment analysis is often driven by an algorithm, scoring the words used along with voice inflections that can indicate a person’s underlying feelings about the topic of a discussion. Sentiment analysis allows for a more objective interpretation of factors that are otherwise difficult to measure or typically measured subjectively, such as:

      • The amount of stress or frustration in a customer’s voice
      • How fast the individual is speaking (rate of speech)
      • Changes in the level of stress indicated by the person’s speech (such as in response to a solution provided by a customer support representative)

In customer service and call center applications, sentiment analysis is a valuable tool for monitoring opinions and emotions among various customer segments, such as customers interacting with a certain group of representatives, during shifts, customers calling regarding a specific issue, product or service lines, and other distinct groups.

Sentiment analysis may be fully automated, based entirely on human analysis, or some combination of the two. In some cases, sentiment analysis is primarily automated with a level of human oversight that fuels machine learning and helps to refine algorithms and processes, particularly in the early stages of implementation.

Examples of Sentiment Analysis

Sentiment analysis is used across a variety of applications and for myriad purposes. For instance, sentiment analysis may be performed on Twitter to determine overall opinion on a particular trending topic. Companies and brands often utilize sentiment analysis to monitor brand reputation across social media platforms or across the web as a whole.

One of the most widely used applications for sentiment analysis is for monitoring call center and customer support performance. As companies seek to keep a finger on the pulse of their audiences, sentiment analysis is increasingly utilized for overall brand monitoring purposes.

Sentiment analysis has been used by political candidates and administrations to monitor overall opinions about policy changes and campaign announcements, enabling them to fine-tune their approach and messaging to better relate to voters and constituents. In brand reputation management applications, overall trends in sentiment analysis enables brands to identify peaks and valleys in overall brand sentiment or shifts in attitudes about products or services, thus enabling companies to make improvements perfectly in-tune with customer demands.

Benefits of Sentiment Analysis

A relative sentiment analysis score provides insight into the effectiveness of call center agents and customer support representatives and also serves as a useful measurement to gauge the overall opinion on a company’s products or services. When sentiment analysis scores are compared across certain segments, companies can easily identify common pain points, areas for improvement in the delivery of customer support, and overall satisfaction between product lines or services.

By monitoring attitudes and opinions about products, services, or even customer support effectiveness continuously, brands are able to detect subtle shifts in opinions and adapt readily to meet the changing needs of their audience.

Challenges and Best Practices for Sentiment Analysis

Language is complex, and as a process for quantifying and scoring language, sentiment analysis is equally complex. What is relatively easy for humans to gauge subjectively in face-to-face communication, such as whether an individual is happy or sad, excited or angry, about the topic at hand, must be translated into objective, quantifiable scores that account for the many nuances that exist in human language, particularly in the context of a discussion. For instance, a word that otherwise carries a positive connotation used in a sarcastic manner could easily be misinterpreted by an algorithm if both context and tone are not taken into consideration.

Given these challenges, sentiment analysis solutions must consider acoustic measurements (the rate of speech, stress in a caller’s voice, and changes in stress signals) in the context of the conversation. Additionally, integrating machine learning into the mix enables sentiment analysis to become more accurate over time, as algorithms learn and adapt to the commonalities in conversations and how the context of conversations can change outcomes.

In call center and customer support applications, sentiment analysis that operates in real-time provides crucial feedback to agents and representatives, allowing them to respond appropriately to impact the outcome of the interaction. When used in conjunction with powerful features like Semantic Building Blocks, companies can easily monitor agent performance by identifying positive patterns such as frustration or dissatisfaction in the early stages of a call followed by positive sentiments in the later portion of the call, representing an agent’s ability to talk-down frustrated customers.

When utilizing the right technology tools and applying it to key business drivers, sentiment analysis is a powerful tool for steering companies and their individual business units to successful outcomes from every customer interaction.

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