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    December 14, 2023

    Call Center Sentiment Analysis: A Guide to Enhanced Customer Insights


    Sentiment analysis is a technique utilizing natural language processing (NLP), machine learning, and computational linguistics to determine the polarity of text and call data, whether it is positive, negative, or neutral. Organizations commonly use it to monitor their customers.By identifying positive and negative sentiments in customer expressions, companies can improve their interactions with customers and take steps to ensure that their actions and behaviors are positive.

    What Is Call Center Sentiment Analysis, And Why Is It Important in a Call Center?

    In call centers, sentiment analysis is used to evaluate customer feedback in various communication channels, such as phone calls, text messages, emails, and chats. It’s all about understanding the emotions and attitudes of customers during their interactions with call center agents.

    Call centers benefit from sentiment analysis in many ways. Through it, they get real-time analysis of customer conversations and conversion of spoken words to text. Supervisors use this to address issues or commend exceptional service promptly. Sentiment analysis tools also extend to survey responses, reviews, and social media, providing insights into overall customer sentiment across all platforms. 

    This data helps in evaluating agent performance, refining scripts and processes, and identifying trends over time. Managers can use sentiment analysis to create visual reports and dashboards, enabling them to make informed decisions and continually improve the customer experience. In addition, automated features like prioritizing issues based on sentiment intensity and deploying chatbots for quick responses contribute to a more proactive and customer-centric approach.

    If you want to improve your call center’s performance, consider using Call Criteria’s advanced sentiment analysis tools. With features like performance dashboards, Call Criteria offers a seamless solution to empower your team and deliver an exceptional customer experience. Take the next step in revolutionizing your call center – try Call Criteria’s sentiment analysis today.

    Call Criteria’s Approach to Call Center Sentiment Analysis

    Sentiment analysis is a powerful tool that has transformed call center quality assurance. With the help of AI, analyzing certain procedures has become easier than ever before. However, it’s important to remember that AI is not a perfect solution for sentiment analysis. While some providers may claim to offer 100% accuracy, the truth is that AI can only provide up to 70% accuracy. 

    But don’t worry, achieving higher levels of accuracy is possible. At Call Criteria, we believe in going the extra mile to ensure the highest level of accuracy possible. That’s why we combine the speed of AI with the precision of human verification to achieve up to 99% accuracy. 

    Benefits of Sentiment Analysis in the Call Center

    Sentiment analysis is a powerful tool that helps call centers in various ways, including:

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    Emotion Detection

    Your customers’ emotions provide valuable insight into their perception of your business. Often, these emotions are displayed during interactions with call center agents. Sentiment analysis tools help to detect these emotions by filtering certain words. Consider the following statement for further understanding.

    Customer: “I’ve been experiencing some issues with your product. It’s frustrating because it’s affecting my work.”

    The sentiment analysis algorithm will categorize this customer’s statement as negative, indicating frustration and dissatisfaction. This real-time insight alerts the call center representative to the customer’s emotional state, prompting them to approach the conversation with empathy and urgency to address the reported issues promptly. 

    Intent Analysis

    Sentiment analysis and intent analysis are two distinct but complementary components in understanding customer interactions. While sentiment analysis focuses on discerning the emotional tone expressed by the customer, intent analysis delves into the purpose or goal behind the communication. The integration of sentiment analysis within intent analysis provides a more comprehensive understanding of customer interactions. For example, consider a customer stating:

    Customer: “Your product isn’t meeting my expectations, and I need a refund.

    In this case, the sentiment analysis tool identifies a negative sentiment, indicating dissatisfaction. Simultaneously, the intent analysis tool recognizes the customer’s intention, which is to seek a refund. With this, the agent on the call can make informed decisions on how to respond to the customer’s concerns.

    Customer Satisfaction Prediction

    If the sentiment analysis tool consistently detects positive expressions, such as satisfaction, appreciation, or joy, in customer reviews, feedback, or interactions, it signals a high likelihood of customer satisfaction. Conversely, the identification of negative sentiments like frustration, disappointment, or dissatisfaction can be an early warning sign of potential issues.

    Improve Agent Sentiment

    It’s important for your agents to control their sentiments when interacting with customers. Some customers may come off as rude and unwilling to cooperate. This may leave the agent frustrated, and if their sentiment is not controlled can make the situation worse. Sentiment analysis gives real-time feedback and suggestions on how to respond in all circumstances. 

    Arranging Data at Scale

    Sentiment analysis tools automate the categorization of vast amounts of textual information based on sentiment. These tools analyze and determine the emotional tone expressed in text data, enhancing efficient organization and prioritization. 

    For instance, in a large dataset of customer reviews, sentiment analysis can swiftly classify feedback as positive, negative, or neutral. This process streamlines the identification of trends, issues, and areas to improve, enabling data-driven decisions.

    Challenges of Call Center Sentiment Analysis Tools

    Sentiment analysis tools provide incredible insights for agents, supervisors, and managers. However, they have shortcomings that often hinder success. 

    Use of sarcasm

    Humans often communicate using sarcasm, one of the things sentiment analysis tools cannot comprehend. For example, a customer may use the word “fantastic” sarcastically to show the exact opposite of what the word actually means. To the sentiment analysis tool, this is interpreted as positive feedback.

    However, to the human ear, the tone used around the word will suggest otherwise and this is why it is important to back all AI data with human verification.


    Negation refers to the presence of words or phrases that express a negative sentiment or the opposite of what might be expected. For example, consider the phrase:

    Original Statement: “I wouldn’t say the product was expensive.”

    Negated statements like this can significantly impact the accuracy of sentiment analysis.


    Multipolarity is when there are multiple sentiments or polarities within a given conversation. For instance, a customer interaction might contain a mix of positive, negative, and neutral sentiments. This complexity could arise when customers express a range of emotions or opinions throughout the conversation. 

    Sentiment analysis tools may struggle to accurately identify and categorize diverse sentiments expressed in a single communication. Having these challenges in mind, Call Criteria’s sentiment analysis tool includes human verification, to avoid misinterpretation and ensure the liability of the data.

    A Tool (by us) to Give You Enhanced Customer Insights

    Improve your contact center’s customer service with sentiment analysis! With Call Criteria’s advanced tools, gain real-time insights into conversations, agent performance, and overall sentiment. Try it today and revolutionize your call center!