What is Sentiment Analysis? Techniques, Benefits, and Implementation

Learn what sentiment analysis is, its importance, and how to implement it using various techniques to gain actionable insights.
9 min read
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By the end of this article, you will understand:

  • What sentiment analysis is and why it’s important
  • The different approaches to sentiment analysis
  • How to implement sentiment analysis using various techniques
  • The advantages and disadvantages of sentiment analysis

Let’s get started!

What is Sentiment Analysis?

Sentiment analysis, also known as opinion mining, is an artificial intelligence (AI) subfield that focuses on understanding the emotions and opinions expressed in text. It uses advanced algorithms and NLP techniques to evaluate and interpret written text sentiments automatically.

Sentiment analysis mainly tries to answer the question: “What feeling or emotion is expressed in this text?”

How Does Sentiment Analysis Work?

At its core, sentiment analysis is extracting meaning from language. Specifically, the emotional meaning. This involves breaking down text and applying various techniques to understand its sentiment. There are three primary approaches to do this:

  1. Rule-Based Approach
  2. Automatic Approach
  3. Hybrid Approach

Let’s delve into the specifics of each approach.

1. Rule-Based Approach

This classic method relies on predefined linguistic rules and lexicons. A lexicon is a list of words and phrases linked to different labels based on their sentiment (positive, negative, or neutral).

Let’s take a look at a step-by-step breakdown of how it works,

Step 1: Tokenization

This is the process of dividing a text into smaller parts called tokens. These tokens can be single words, phrases or even punctuation marks. This is the basic unit of analysis and good tokenization is essential in finding the right words for sentiment assessment.

Step 2: Lexicon lookup

Each token is compared against the lexicon. This lexicon acts as a dictionary, associating words with their pre-determined sentiment scores based on their emotional state.

For example, words like “love,” “amazing,” and “delightful” would have positive scores, while “hate,” “terrible,” and “disgusting” would have negative scores.

Step 3: Rule application

While lexicons provide a foundation, they don’t capture the full complexity of language. Rule-based systems incorporate linguistic rules to refine the analysis. These rules consider:

  • Negation: Words like “not” or “never” can change the sentiment of a word (e.g., “not good” is negative).
  • Intensifiers: Words like “very” or “extremely” can strengthen the sentiment (e.g., “very happy” is more positive than “happy”).
  • Contextual Dependencies: The way one word relates to another influences sentiment. For instance, in the phrase “not bad,” the word “bad” has been negated and gives out positive sentiments.

Step 4: Sentiment aggregation

After individual tokens are scored, the rule-based system combines these scores to determine the overall sentiment of the text. This might involve simple summation, weighted averages, or more complex algorithms considering the position and relationships between words.

2. Automatic Approach

This automatic approach is often referred to as the machine learning approach. It has revolutionized how we decipher emotions in text. Instead of using predefined rules, it relies on algorithms that learn from millions of labelled data sets.

These algorithms can identify patterns in language, automatically classify text as either positive, negative, or neutral, and even detect specific emotions or opinions.

Let’s take a look at a step-by-step breakdown of how this works,

Step 1: Data collection and preparation

In this first step, a diverse range of text data is gathered and manually evaluated to assign a sentiment label to indicate its emotional tone. The data is then cleaned and standardized to ensure the model focuses on meaningful patterns.

Step 2: Feature extraction

Then, this cleaned data is transformed into numerical representations that algorithms can process. This typically involves converting words into vectors, often using techniques like bag-of-words, TF-IDF, or word embeddings. These vectors capture the semantic relationships between words and provide valuable information for the model.

Step 3: Model training

Machine-learning model training is dependent on the extracted features and labelled data. In this step, the model learns to associate specific patterns in the text with their corresponding sentiment labels.

Various algorithms can be used in this step, including Naive Bayes, Support Vector Machines, or more complex deep learning models like Recurrent Neural Networks (RNNs).

Step 4: Sentiment prediction

Once developed, the trained model can be used on different texts. It examines the content and extracts features, using these patterns to determine the feeling of the text. This prediction can be a binary classification (positive or negative), a multi-class category (positive, negative, neutral), or even an elaborate evaluation, such as happy or angry.

3. Hybrid Approach

The hybrid approach optimizes sentiment analysis by combining the merits of rule-based and machine-learning techniques. Using lexicons and linguistic rules with a machine learning algorithm can make this method more accurate and better comprehend complex language.

While it requires more technical effort, this approach offers a more robust solution for deciphering complex emotions in text.

Why is Sentiment Analysis Important

While it is useful in any field, sentiment analysis has been particularly beneficial in business for better decision-making. For example, customer feedback, which comes from different methods like surveys, reviews, or social media, can be automatically analyzed to determine the customer sentiment toward the products and services they provide.

Additionally, this mainly allows them to:

  • Enhance brand reputation: By tracking what people say online, they can anticipate how they want to be perceived and use this opportunity to care for customers by building loyalty.
  • Provide real-time customer support: Companies can focus on real-time issues that may be discovered from clients’ emotional responses during communication sessions.
  • Personalize marketing efforts: Tailor campaigns and recommendations based on customer preferences and opinions.

Different Types of Sentiment Analysis

Sentiment analysis isn’t just about labelling the text as positive, negative, or neutral. It’s a versatile tool capable of capturing a wide range of emotions, intentions, and even urgency within text.

Here are some of the most common types of sentiment analysis used to extract nuanced insights from textual data:

1. Graded analysis

Graded sentiment analysis assigns scores on a scale, providing a more nuanced view of sentiment intensity. This approach helps gauge the strength of emotions expressed in the text.

For example, a review might be labelled as “very positive,” “slightly positive,” “neutral,” “slightly negative,” or “very negative.”

2. Emotion detection

This type goes a step further into particular emotions, classifying the text into groups like joy, anger, sadness, fear, or surprise. By recognizing these, companies can better understand customer responses, which helps them respond to specific issues accordingly.

For example, if you can identify any frustration in a customer complaint, you can address the issue immediately and prevent escalation.

3. Aspect-based analysis

This focuses on identifying sentiment towards specific aspects or features of a product, service, or topic. For instance, in a hotel review, aspect-based analysis might determine positive sentiment towards the location but negative sentiment towards the cleanliness.

4. Intent-based analysis

This analysis type can detect the motive behind a text, whether the author is trying to express an opinion, make a recommendation, ask a question, or express a need. Understanding intention is important in customer service, market research, and targeted advertising.

For example, a customer tweets, ‘I wish Company X’s product had a longer battery life.’ This indicates dissatisfaction and a desire for improvement (intent to recommend a feature change). This helps Company X handle the negativity and use this feedback to improve their products.

Advantages and Disadvantages of Sentiment Analysis

Sentiment analysis is a powerful tool with both strengths and weaknesses. Understanding these can help businesses make the most informed decisions about how and when to leverage this technology.


1. Deeper understanding of customer opinions and emotions

As discussed in the above sections, sentiment analysis provides a granular look into customer thoughts and feelings beyond simple satisfaction scores. This deeper understanding empowers businesses to:

  • Address specific pain points: Identify and resolve specific issues causing customer dissatisfaction.
  • Replicate successes: Double down on features or services that customers rave about.
  • Tailor offerings: Develop new products and services that align with customer preferences and emotional needs.

2. Real-time insights

Unlike traditional feedback methods like surveys, sentiment analysis provides real-time insights into customer opinions. This is crucial for maintaining a positive brand image and fostering customer loyalty in the fast-paced digital landscape.

3. Scalability

This can efficiently process massive amounts of data from various sources, including social media, reviews, and surveys. This scalability allows businesses to analyze vast amounts of customer feedback that would be impossible to process manually.

4. Objectivity and Consistency

By removing human bias from the analysis, sentiment analysis ensures consistent and objective results. This is particularly valuable when dealing with large volumes of data or when comparing sentiment over time.


1. Contextual understanding

One of the main challenges in sentiment analysis is the struggle to understand sarcasm, irony, or humour. Cultural references and domain-specific jargon can also lead to misinterpretations. Researchers and developers are constantly improving algorithms to understand context better and mitigate this.

2. Data quality and bias

The accuracy of sentiment analysis heavily relies on the quality of the data it’s trained on. The results can be skewed if the training data is biased or incomplete.

While sentiment analysis provides unbiased results since humans are not involved in the analysis, it can still be biased if the given dataset is biased.

3. Subjectivity of language

Sentiment is subjective, and different individuals might interpret the same text differently. For example, the phrase “This product is fine” might be seen as positive by one person (meaning “good enough”) but neutral or even slightly negative by another (meaning “just okay”).

This inherent subjectivity makes it challenging to establish a universal standard for sentiment scoring that accurately reflects everyone’s interpretation.


Sentiment Analysis is a transformative tool for businesses. It allows them to analyze customer opinions and extract meaningful insights from text. A solid sentiment analysis model can take a business to the next level by offering products and services that meet customer needs.

However, building a good sentiment analysis model is challenging, with finding a good scraping tool or a high-quality dataset being one of its toughest parts. You need to ensure the accuracy and quality of the data to get an unbiased output.

Bright Data’s datasets are a great place to get high-quality datasets for your sentiment analysis projects. It offers various datasets across various industries and domains, providing free samples and a user-friendly environment for browsing and purchasing the datasets you need after signing up.

Register now and download your free dataset samples!

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