Sentiment Analysis in Customer Service: Understanding Human Emotions


The ubiquitous influence of Artificial Intelligence (AI) in customer service has helped enterprises include real-time-narrative mapping in chatbots. With this innovation, chatbots are capable of operating based on intent recognition and understanding human emotions; also known as Sentiment Analysis.

Sentiment Analysis is a natural language processing technique used to regulate whether data is neutral, positive, or negative. It is the process of determining whether a text conveys a good or bad statement about a topic or a product. Many companies use sentiment analysis as they don’t have to spend endless hours tagging customer data such as reviews, social media comments, support tickets, and survey responses. Sentiment Analysis is very much beneficial for the company as it keeps a track of its brand reputation on social media, gets information from customer feedback, and much more. 

What is Sentiment Analysis?

Sentiment Analysis refers to the process in which it detects positive or negative sentiment in text. It is becoming an essential tool to many companies as it helps them monitor customer sentiments. 

There are particularly four types of Sentiment Analysis. 

Fine-Grained Sentiment Analysis- This is one of the most commonly used sentiment analysis by most companies or organizations. The categories include-

    1. Very positive 
    2. Positive 
    3. Neutral 
    4. Negative 
    5. Very negative

This is generally known as fine-grained sentiment analysis and can be used to elucidate five-star ratings. That is for example- very positive=5 stars and very negative= 1 star.

  • Emotion Detection- This type of sentiment analysis detects various emotions such as happiness, anger, sadness, frustration, and so on. Most emotion detection systems use lexicons or complex ML algorithms.
  • Aspect-based sentiment- When you’re looking through some product reviews, you will want to know which specific aspects or features are customers mentioning in a positive, neutral, or negative kind of way. That’s where Aspect-based sentiment falls in. 
  • Multilingual Sentiment Analysis- This is a difficult type of sentiment analysis that includes understanding and preprocessing of different languages. 

Sentiment Analysis uses NLP(Natural Language Processing) and other algorithms like 

  • Rules-based systems- which is basically using a set of manually crafted rules.
  • Automatic systems- relies on ML techniques to learn from data.
  • Hybrid systems- this type of system combines both rule-based and automatic approaches.

How Sentiment Analysis Can improve Customer Service:

Using sentiment analysis you can examine the information, to recognize the emotions, attitudes, and tones of customers throughout all social media platforms, including emails, posts, and text conversations. You can then make use of this information, get more insights on what you need to provide or take care of your business/company to make it more beneficial and customer-friendly. 

There are many benefits to utilizing sentiment analysis in customer service, they include-

  • Sentiment Categorization- Sentiment analysis helps you to work with more specific/precise data. Hence, you can use this data to understand customer feedback. Further, you can categorize the sentiment, which allows you to know how customers really feel about your employees, physical and digital stores, websites, products, and services.
  • Problem Identification- Unhappy customers mostly tend to let out their anger on “social media”, if they’re not satisfied with any services/products your company has provided. So with the help of sentiment analysis, you can keep track of your brand perception and avoid the potential setback. 
  • Competitor Analysis- Sentiment analysis also helps you track other competitors and can assist you in exploring how they are perceived in comparison to you. You can also make use of sentiment analysis to understand the trending topics on social media. Thus you can realize and get to know what your competitors are up to and which people they are targeting. This can be pivotal in predicting market trends and can help enterprises in providing a customer-centric service. 
  • Clearer Understanding of Customer Feedback- In order to get to know how people feel about your products or services, you can take the help of sentiment analysis. It assists you in improving your schemes and helps in reducing drawbacks by identifying them. You can track the customer feedback and understand the actions needed to be taken to prevent it in the future and try to deliver a positive customer experience. 
  • Better Call Routing- Whatever decisions your conversational Interactive Voice Response (IVR) makes, sentiment analysis plays an important role in it. This helps to resolve issues more quickly and allows the staff to learn how to deal with tough, and challenging situations. 
  • Legislative Compliance- Only relying on texts or spoken words isn’t really or always satisfying for compliance purposes. Sentiment analysis lays out a deeper understanding and meaning, hence assisting you to understand the truthfulness of an interaction. This helps to maintain your company’s reputation. 

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