Understand Your Customers: Sentiment Analysis for Emotional Bonds

Sentiment analysis is monitoring customers to understand their needs and anxieties.

Businesses now have access to more data about their customers. There are many ways to measure customer sentiment, such as email, social media, websites, online surveys, customer service chats, reviews, or available sources.

Sentiment analysis systems are useful for understanding customers, providing stronger customer experiences, and improving brand reputation.

So, understand your customers through sentiment analysis for a stronger emotional bond.

What Is Customer Sentiment Analysis

Sentiment analysis or opinion mining is the process of analyzing digital text to determine whether the emotional tone of the message is positive, negative, or neutral.

This process is one of the applications that apply Natural Language Processing (NLP) technology. It aims to train computer software to understand text like humans.

Now, sentiment analysis is widely used to measure customer satisfaction, brand monitoring, political sentiment, market analysis, social media monitoring, and more.

Also Read: Navigating Ethical Design Challenges in UX

Why Does Sentiment Analysis Matter

Sentiment analysis is a business intelligence tool that helps you improve products and services. Here’s why sentiment analysis is important.

1. Provides More Objective Results from Customer Reviews

Businesses can avoid personal biases associated with human reviewers. This can be prevented by using artificial intelligence (AI)-based sentiment analysis tools. As a result, companies get objective results when analyzing customer opinions.

For example, if a customer expresses a negative opinion along with a positive opinion in a review, a human rating the review may label it negative before arriving at the positive words. AI-enhanced sentiment classification helps sort the text objectively. So this does not happen and both sentiments are reflected.

2. Achieve Greater Scalability from Business Intelligence Programs

Businesses are continuously mining information from a large amount of unstructured data, such as emails, chatbot transcripts, surveys, customer relationship management records, and product feedback.

Cloud-based sentiment analysis tools allow businesses to scale the process of uncovering customer emotions in textual data at an affordable cost.

3. Perform Real-Time Brand Reputation Monitoring

Businesses must respond to potential crises or fast-changing market trends. You should rely on sentiment analysis software to learn what customers feel about the company’s brand, products, and services in real time and take immediate action based on the findings.

If negative sentiments appear for certain keywords, your team can set the software to send alerts.

Types of Sentiment Analysis

Different types of sentiment analysis can be used depending on your business needs. Here are the three most widely used types of sentiment analysis.

1. Fine-Grained (Graded)

Fine-grained or graded sentiment analysis is a type of sentiment analysis that categorizes text into different emotions and the level of emotion expressed.

The emotions are then rated on a scale of 0 to 100. This is similar to how websites use star ratings to measure customer satisfaction.

2. Aspect-Based (ABSA)

Aspect-based Sentiment Analysis (ABSA) examines a text to identify a single aspect of a product, service, or customer experience that a business wants to analyze.

ABSA can help businesses understand how their products succeed or fail to meet customer expectations.

3. Emotional Detection

Emotion detection seeks to understand an individual’s psychological state behind a text, frame of mind, and intentions. This analysis is more complex than the previous one. It is usually used to better understand a person’s motivations or emotional state.

Emotion detection can identify specific emotions in a text, such as frustration, indifference, anxiety, and shock.

Also Read: Navigating User Research: 9 Cognitive Biases to Avoid in UX

How Sentiment Analysis Works

Sentiment analysis uses Natural Language Processing (NLP) and Machine Learning (ML) technologies. It aims to train computer software to analyze text like humans.

1. Rule-Based Sentiment Analysis

In a rule-based approach, the software is trained to classify certain keywords in a block of text based on groups of words or lexicons that describe the author’s intent.

For example, words in a positive lexicon may include “affordable”, “fast”, and “well-made”, while words in a negative lexicon may include “expensive”, “slow”, and “poorly-made”.

The software will scan the words in the positive or negative lexicon and calculate a total sentiment score based on the volume of words used and the sentiment score of each category.

2. Machine Learning Sentiment Analysis

Machine learning (ML) is used to measure sentiment in a block of text by using words that appear in the text and their order. Some of the most commonly used algorithms are:

• Linear regression: A statistical algorithm that describes a value (Y) based on a set of features (X).
• Naive Bayes: An algorithm that uses Bayes’ theorem to categorize words in a block of text.
• Support vector machines: A fast and efficient classification algorithm used to solve two-group classification problems.
• Deep learning (DL): Also known as artificial neural networks, deep learning is an advanced machine learning technique that connects multiple algorithms to mimic the functioning of the human brain.

3. The Hybrid Approach

Hybrid approaches to text analysis combine ML and rule-based capabilities for accuracy and speed. While accurate, this approach requires more resources, such as time and technical capacity.

Conclusion