Sentiment Analysis

Sentiment analysis is the process of analyzing textual data to check its emotional tone i.e.; whether it expresses a positive, negative, or neutral sentiment. Companies have massive amounts of data about their customers from emails and posts of X to feedback, online survey responses, reviews, and chats with customer service representatives. These data can be used to extract meaningful insights to support their business decisions. Sentiment analysis using NLP & Machine Learning (ML) techniques that can help companies understand their customer better, improve their product reputation, and deliver good customer experience.

In this blog, we will explore the value of sentiment analysis, and its use cases. Let’s get started with WeCloudData!

Why is sentiment analysis important?

We are living in the age of social media. Social media is the best platform where the opinions of customers have a major impact on the reputation and success of a brand or product. Sentiment analysis is also known as opinion mining. Companies need to know the dynamic of their customers’ opinions about their products and services in real time to understand them better and improve their products accordingly. Sentiment analysis is the best tool used by companies in this regard.

Benefits of Sentiment Analysis

Here are the key benefits of sentiment analysis;

Provide Unbiased Insights

When a company analyzes customer opinions, AI-powered sentiment analysis eliminates human bias and guarantees accurate and consistent results. AI analyzes each part of a review objectively, compared to human reviewers who could unintentionally concentrate on certain parts while ignoring others.

For example in this feedback:

“The customer support team was very polite, but it took too long to resolve my issue.”

A customer service manager might dismiss the discouraging part( long resolution time) of the review and be positively biased toward politeness. However, accurate sentiment analysis tools sort and classify text to pick up emotions objectively.

Real-time results

In today’s digital age, customer feedback can make or break a brand, as the customer is always right. Businesses can monitor public opinion in real time with AI-powered sentiment analysis, which enables them to take preventative measures to address problems before they become more serious.

For example, the sentiment analysis software can instantly notify the team if a company introduces a new feature and social media sentiment increases negatively, allowing them to solve the issues before they become a PR disaster.

Build Better Products and Services

Sentiment analysis helps companies improve their products and services based on customer feedback. Sentiment analysis software identifies real-world objects called entities that customers associate with negative sentiment.

In the above example, the important entities identified by sentiment analysis software are “customer support” and “long resolution time”. This analysis enables businesses to optimize their support processes, reduce wait times, and improve customer satisfaction.

How Sentiment Analysis Works

Sentiment analysis uses Natural Language Processing NLP technologies along with Machine Learning to train software to understand textual data mimicking human understanding.

Sentiment analysis is an application of natural language processing (NLP) technologies that train computer software to understand text in ways similar to humans.

There are three techniques major used behind sentiment analysis tools:

• Rule-based Sentiment Analysis: Uses predefined rules and lexicons.
• Machine Learning-based Sentiment Analysis: Learns from labeled text data that the model is trained on.
• Hybrid Approach: Combines rule-based and ML techniques

Rule-based Sentiment Analysis

The rule-based approach uses predefined rules and word lists (lexicons) that represent positive or negative sentiment to categorize text. For example, a positive lexicon contains words like “excellent,” “affordable,” and “reliable,” and a negative vocabulary or lexicon contains words such as “Terrible,”,” Poor” “Slow,” and “Overpriced.”

Example:

A product review:

“The laptop is lightweight and fast, but the battery drains quickly.”

The rule-based sentiment analysis software will identify “lightweight” and “fast” as positive phrases; “drains quickly” will be recognized as a bad phrase; and an overall sentiment score will be assigned depending on the ratio of positive to negative words.

Machine Learning-based Sentiment Analysis

Machine Learning uses different algorithms (Naïve Bayes, Support Vector Machines, Neural Networks) to identify sentiment patterns in the text without depending on predefined word lists. The model performance improves as models continue to “learn” from the data they are fed.

How It Works

Instead of manually assigning words as “positive” or “negative,” as in the rule-based method  ML models analyze:

• Word sequences and relationships
• Words context within sentences
• Data patterns from labeled datasets it trained on or learned from

Example:

A review:

“I waited an hour for support, but the agent was extremely helpful!”

A rule-based model might classify this as negative due to “waited an hour,” but a Machine Learning model can recognize that “extremely helpful” carries a stronger sentiment, leading to a neutral or slightly positive classification.

Hybrid Approach

Hybrid-based approach for sentiment analysis uses both rule-based and machine learning-based approaches to optimize accuracy and speed during textual analysis. This approach is more resource-intensive as requires technical capabilities and time.

Sentiment Analysis Use Cases

Sentiment analysis is used in almost every domain here are some use cases in multiple domains;

• Brand monitoring
• Improving customer service
• Conducting Market research
• Track campaign performance
• Building a stronger brand presence

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• Hands-On Learning: The course emphasizes practical, hands-on learning with real-world examples.
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Stay tuned for more blogs and tutorials on technical concepts, to learn more about WeCloudData. Happy Learning!