What Is Sentiment Analysis? Learn About Its Types and Functions

Sentiment Analysis is a method used to understand the overall tone of opinion in text, for example, whether customer responses to a brand tend to be positive, negative, or neutral.

This strategy applies to various data sources, from customer reviews and social media comments to online brand mentions. Its application is becoming increasingly relevant as users’ habits in searching for and evaluating information continue to evolve.

According to a December 2025 survey of 1,030 AI users in the United States, at least 57% used AI to narrow their choices, 53% to compare products, and 50% to help with final purchasing decisions.

So, how does the sentiment analysis method work, and what is sentiment analysis in AI? This article will cover it in full, so be sure to read through to the end!

What Is Sentiment Analysis?

Simply put, sentiment analysis is the process of classifying opinions or responses in text based on the underlying attitudes or emotions expressed. The results generally include positive, negative, and neutral sentiments.

So, what is sentiment? This term usually refers to the attitude, opinion, or feeling that a person expresses toward a specific object or topic. For example, when a customer writes that the product quality is good and the ordering process is easy. That response can be categorized as sentiment.

Conversely, negative sentiment is a response that indicates dissatisfaction with a particular product, service, or experience. By processing large amounts of data, a business can identify patterns in customer opinions without reviewing and categorizing them one by one.

Broadly speaking, this process relies heavily on NLP and machine learning to analyze text and determine sentiment trends.

How Does Sentiment Analysis Work?

Sentiment analysis applies NLP and machine learning to process text and classify sentiment based on identified language patterns. It can be implemented using a rule-based approach, machine learning, or a combination of both. Generally, the workings of sentiment analysis can be understood through the following stages:

1. Collecting Data

The system retrieves text data from relevant sources, such as customer reviews, surveys, social media, or customer service conversations.

2. Performing Pre-Processing

The data is then cleaned and prepared before analysis. This stage includes tokenization, irrelevant word removal, and word form normalization.

3. Analyzing Language Patterns

The system then identifies words, phrases, and patterns in the text to determine information related to sentiment.

4. Classifying Results

The text is then grouped based on sentiment type or a specific sentiment level according to the model used.

The final result will be a simple classification, such as positive, negative, and neutral, or a more specific analysis based on certain aspects.

Read also: Google AI Search Mode: Impact & Website Performance Optimization

What Are the Approaches to Sentiment Analysis?

In general, there are three main approaches to sentiment analysis: rule-based, machine learning, and hybrid. Here is a detailed explanation:

• Rule-based: Uses a lexicon and predefined rules to associate words or phrases with specific sentiment categories.
• Machine learning: Uses models trained on data to recognize language patterns and perform classification.
• Hybrid: Combines rule-based and machine learning approaches to leverage the strengths of both.

Besides these approaches, sentiment analysis can also be made more specific based on the object being analyzed. Here is the explanation:

Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is used to determine sentiment toward specific aspects of a product or service. For example, a customer might give a positive review of a smartphone camera’s quality but complain about its battery life.

This approach can help your business identify which specific aspects receive positive or negative feedback, rather than just determining the overall sentiment of a review.

Why Is Context Important in Sentiment Analysis?

Without context, a system can easily misinterpret data. This is because certain words do not always convey the same sentiment. Sarcasm, negation, and sentences containing more than one sentiment can lead to inaccurate classification results. So, what are some examples of sentiment analysis?

For example, when a customer writes, “The service was fast, but the product was disappointing.” This sentence expresses different sentiments toward different aspects, so it cannot be adequately analyzed based on just one word or a single category.

What Are the Applications of Sentiment Analysis?

Businesses can use sentiment analysis for various purposes, including customer service, brand monitoring, market research, and even campaign evaluation. Here are some benefits of sentiment analysis you should know:

• Customer Service: AI-powered chatbots can automatically detect urgent complaints and escalate them to staff before the issue escalates.
• Brand Monitoring: A company’s public relations team can monitor spikes in negative sentiment on social media in real time, allowing them to address reputation crises in a matter of hours, not days.
• Market Research: Product teams can compare their brand’s sentiment scores with those of competitors on the same topics, such as fund transfer speed or service fees.
• Campaign Evaluation: Your business’s marketing team can also check whether sentiment toward a newly launched ad aligns with the target audience and then adjust the message if the results fall short of expectations.

A real-world example is a bank that, upon detecting a surge in negative sentiment regarding a new mobile banking feature, can immediately review app reviews to identify the root cause, rather than waiting for complaints to pile up on social media and lead to a reputation crisis.

The Difference Between Sentiment Analysis and Semantic Analysis

Sentiment analysis and semantic analysis are both related to natural language processing, but they have different focuses. Sentiment analysis focuses on trends in opinions and emotions, while semantic analysis focuses on meaning and the relationships between elements in a text. Here is a summary table comparing the two for easier understanding:

In short, sentiment analysis and semantic analysis are indeed complementary. Semantic analysis helps to understand context, while sentiment analysis provides information about emerging opinion trends.

The Relationship Between Sentiment Analysis and NLP

NLP (Natural Language Processing) is a model that helps computers process and analyze human language. Therefore, NLP for sentiment analysis provides the foundation behind the entire analysis process. Without NLP, search engines could not parse sentences, recognize keywords, or learn patterns in human language from millions of lines of text.

Meanwhile, machine learning can be used to train models to recognize patterns from available data. Therefore, NLP and machine learning support the analysis process, rather than being the same category as sentiment analysis.

The Relationship Between Sentiment Analysis and AEO and GEO

Sentiment analysis can help your business understand how people are talking about a brand, product, or specific topic across various digital channels. Meanwhile, AEO and GEO focus on how information about a brand can be found, understood, and utilized by search engines and AI.

In other words, sentiment analysis is not directly an AEO or GEO technique. Its results are best used as strategic input to understand “what the audience is saying” before a business optimizes how AI represents that brand.

However, positive sentiment is not enough on its own to ensure a brand appears or is represented well in AI-generated answers.

The results of this analysis must also be combined with entity signals, information relevance, citations, topical authority, and other factors that help AI understand and recognize the brand as a reference source.

To optimize these aspects, cmlabs provides AI Search Optimization Services (GEO/AEO) that include optimizing entity signals, semantic relevance, data structure, and topical authority, as well as monitoring AI visibility such as mentions, citations, sentiment, SEO strategies for LLMs, and share of voice.

If you want to customize your strategy to your business needs, do not hesitate to contact our team for a free consultation to discuss your business challenges and opportunities in more detail.

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