What is Sentiment Analysis? A Simple (But Complete) Guide
by
Bill Widmer
December 16, 2024
Sentiment analysis is the process of using natural language processing (NLP), text analysis, and computational linguistics to identify, extract, and quantify subjective information from text.
In simpler terms, it’s a method of uncovering the emotions behind words.
For businesses, sentiment analysis is a powerful tool to gauge customer’s feelings, turning open-ended text data into actionable insights. This enables companies to improve their products, services, and overall customer experience based on real-world feedback.
In this guide, you’ll learn how sentiment analysis works, why it’s important for businesses, and the fastest and easiest way to analyze text for sentiment at scale. Let’s dive in!
How Sentiment Analysis Works
Sentiment analysis starts with gathering raw text data, then categorizing its emotional tone.
Opinion mining, a related concept, involves analyzing text data to determine emotional tone and categorize opinions, playing a crucial role in fields like e-commerce and marketing.
Sentiment Categories
Analysts are looking for positive, negative, or neutral sentiment. Sometimes, there is also mixed sentiment, meaning one response may indicate both positive and negative emotions.
Consider these examples:
Positive sentiment:“I absolutely love this product! It exceeded all my expectations and works perfectly.” Words like love and exceeded expectations clearly indicate a positive experience.
Negative sentiment:“Terrible experience. It broke after one use, and customer service was unhelpful.” Terms like terrible and unhelpful highlight dissatisfaction.
Neutral sentiment: “The product arrived on time and was packaged as expected.” This statement neither expresses positive emotions (like praise or satisfaction) nor negative emotions (like disappointment or frustration). Instead, it simply provides factual information without attaching any sentiment to it.
Mixed sentiment:“I loved the product, but the customer support was terrible.” Here, sentiment analysis needs to identify positive sentiment about the product and negative sentiment about the customer support, categorizing mentions into negative or positive sentiment.
At first, sentiment seems obvious.
But sentiment analysis at scale involves analyzing hundreds or thousands of such responses, often with mixed sentiments, to get a comprehensive understanding of customer emotions.
Brief History of Sentiment Analysis
The journey of sentiment analysis began in the field of natural language processing (NLP) several decades ago. The first sentiment analysis tools emerged in the 1990s, but they were limited in their ability to accurately interpret text data.
Until recently, you had to train machine learning algorithms on your data to have good accuracy. This took a lot of time, effort, and money.
With Large Language Models (LLMs), this is no longer needed.
Now you can use AI-powered tools like Blix for sentiment analysis and get results faster and easier than you could ever hope to do manually.
Techniques Used in Sentiment Analysis
The techniques for performing sentiment analysis can be broadly divided into rule-based, machine learning-based, and LLM-driven approaches.
Sentiment analysis work involves using algorithms from natural language processing (NLP) and machine learning (ML) to analyze text and categorize sentiment as positive, negative, or neutral.
Rule-Based Approaches: These rely on predefined rules and dictionaries of positive and negative words. For example, words like excellent or terrible are scored based on their inherent sentiment. While simple and interpretable, rule-based methods often struggle with context, sarcasm, or domain-specific language.
Machine Learning-Based Approaches: Machine learning models are trained on labeled datasets to classify sentiment and improve over time as they are exposed to more data. However, they are limited by what they’ve been trained on and can struggle with context and complex tasks or new topics.
Using Large Language Models (LLMs): Advances in NLP, such as GPT models, enable more accurate sentiment analysis. LLMs can understand context, handle mixed sentiments, and even detect complex emotional undertones.
Blix leverages LLMs to make sentiment analysis more accurate and scalable. Our sentiment analysis system can detect sarcasm, confront contradictory expressions, and evaluate specific sentiments associated with different aspects of a product or service. It’s also able to do sentiment analysis across languages.
3 Steps of Sentiment Analysis
There are three main steps in sentiment analysis:
Step 1. Text Collection and Preprocessing
The first step is to collect the text data. Sources can be customer feedback forms, online reviews, social media platforms, surveys, and support tickets, to name a few.
In traditional sentiment analysis, the next step is preprocessing the text. Preprocessing typically involves several steps: removing irrelevant characters (like special symbols or HTML tags), converting text to lowercase to ensure uniformity, removing stopwords (common words like "and," "the," etc.), and sometimes even stemming or lemmatizing words to reduce them to their root forms to prepare the data for the analysis that follows.
Fortunately, modern sentiment analysis tools like Blix make this process far easier. Blix uses advanced Large Language Models (LLMs) to automatically handle all the preprocessing for you, letting you skip the tedious tasks and focus directly on gaining insights from your data.
Step 2. Determining The Meaning Of The Words
Once the data is collected and cleaned, the next step is to uncover the meaning behind the words.
One thing we look at is polarity, which tells us if the sentiment is positive, negative, or neutral. For example, in the sentence “This app is fantastic,” the word “fantastic” clearly shows a positive feeling.
We also pay attention to intensity, which measures how strong the emotion is. Saying something is “amazing” feels much stronger than just calling it “good.”
But sometimes, meaning isn’t so obvious.
For instance, the phrase “not bad” might sound negative at first, but it’s actually positive when you think about it. This step helps uncover those hidden meanings so we don’t miss anything important.
By focusing on these clues, you can better understand how people really feel—whether they’re thrilled, disappointed, or somewhere in between. This makes sentiment analysis more accurate and insightful.
Step 3. Sentiment Classification
The final step is to analyze sentiment and classify the text’s emotional content into the categories we mentioned above: positive, negative, or neutral.
This is often done using algorithms trained on large datasets. For example, a machine learning model might have been trained on thousands of labeled examples, where each piece of text was manually classified.
Machine learning models and large language models (LLMs) likeChatGPT excel at this stage because they can analyze nuanced contexts, mixed sentiments, and complex linguistic patterns. Unlike traditional rule-based systems, which rely on static dictionaries of positive and negative words, advanced models learn from context, improving over time.
However, ChatGPT wasn’t built to be a sentiment analysis tool. If you’re looking for an AI-powered sentiment analysis tool, give Blix a try.
Types of Sentiment Analysis
While these are the basic steps of sentiment analysis, there are different types with varying depth:
Polarity-Based Analysis categorizes text as positive, negative, or neutral. It’s the simplest and most commonly used form of sentiment analysis. If you’re looking for a high-level understanding of customer sentiment, polarity-based analysis is a straightforward choice.
Emotion Detection goes a step further by identifying specific emotions like joy, anger, sadness, or surprise.
Fine-Grained Sentiment Analysis provides a more nuanced understanding by categorizing sentiment on a scale, such as very positive, slightly positive, neutral, slightly negative, or very negative.
The complexity of your data and research goals play a role in which type of analysis you use. In general, you will probably be using polarity-based analysis, as it is the most straightforward and beneficial for business use.
However, things get a little more complex when you introduce mixed sentiments based on different characteristics of a product or service. Let us explain…
Aspect-Based Sentiment Analysis (ABSA)
Aspect-Based Sentiment Analysis (ABSA) focuses on evaluating specific aspects of a product or service, providing deeper insights compared to analyzing the overall sentiment of a response.
For instance, in a review stating, “The phone’s battery life is amazing, but the camera is terrible,” ABSA isolates sentiment for each aspect, identifying very positive sentiment about the battery and very negative sentiment about the camera.
This granular approach reveals much more valuable information than treating the entire response as one undifferentiated piece of feedback, where mixed sentiments might cancel each other out or lead to a misclassification.
While performing ABSA manually at scale is highly challenging and time-intensive, advancements in AI and large language models (LLMs) have made it significantly more efficient and accessible. These tools empower businesses to extract actionable insights from even the most complex datasets, enabling precise understanding and better decision-making.
Let’s look at a few real-world examples of ways businesses can use sentiment analysis to improve…
Analyzing open-ended responses in net promoter score (NPS) surveys allows companies like Airbnb to discover why certain users love the platform and why others churn.
Positive sentiment often reveals trust in the booking process, while negative feedback may highlight issues like poor host communication, guiding the company to address these pain points effectively.
Or look at employee engagement surveys, where sentiment analysis reveals the emotional tone behind feedback. Companies like Google use it to identify and address burnout or dissatisfaction among their teams. By analyzing comments, they’ve uncovered recurring themes, such as work-life balance concerns, and implemented changes like flexible work policies to improve morale and retain top talent.
In the realm of online reviews, sentiment analysis is invaluable for businesses like Amazon, which processes millions of customer reviews daily.
For instance, a product showing a consistent pattern of negative sentiment about its durability may prompt Amazon to work with suppliers to improve quality, while celebrating positive mentions of features like usability.
Similarly, hotels like Hilton use sentiment analysis to identify frustrations such as long wait times at check-in and adjust processes to enhance the guest experience.
Challenges and Limitations of Sentiment Analysis
While sentiment analysis offers powerful insights, it faces several challenges that must be addressed for accurate and reliable results:
Manual Challenges:some text
Cognitive Demand: Performing sentiment analysis and verbatim coding manually is mentally taxing.
Linguistic Subtleties: Analysts must carefully navigate nuanced language.
Consistency: Ensuring uniform analysis across large datasets is difficult.
Bias and Time: Remaining unbiased while handling the workload is both time-consuming and cognitively demanding.
Algorithmic Challenges:some text
Sarcasm Detection: Sarcasm is notoriously hard to identify. For example, “Oh, just wonderful—another delay” can be misinterpreted as positive without context.
Negations: Phrases like “not terrible” express positivity but can be misclassified if not properly handled.
Ambiguity and Mixed Sentiment: Statements often combine positive and negative emotions, making it hard to assign a single sentiment label.
Cultural and Language Variations:some text
Sentiment expressions vary widely across languages and cultures.
A phrase that’s positive in one culture might carry a different sentiment in another, necessitating localization and careful training for accurate interpretation.
These challenges emphasize the need for advanced AI-powered solutions capable of handling linguistic subtleties, domain-specific terminology, and cultural diversity to deliver reliable and scalable sentiment analysis. AI solutions like Blix benefit from being able to handle multilingual sentiment analysis from survey responses using different languages, and presenting results in your native language.
How Blix Makes Sentiment Analysis Tools Fast and Easy
By leveraging large language models, Blix makes sentiment analysis faster, more accurate, and scalable. It handles the nuances of mixed sentiments, sarcasm, and domain-specific language, turning customer feedback, social media data, and more into actionable insights with ease.
Accuracy depends on the method used. Rule-based approaches achieve basic accuracy, while advanced machine learning and LLM-based systems can exceed 90% accuracy.
What industries use sentiment analysis the most?
Industries such as retail, technology, finance, hotels, and healthcare commonly rely on sentiment analysis to understand customer feedback and improve service.
Can sentiment analysis detect sarcasm?
Detecting sarcasm is challenging, but advanced NLP techniques and LLMs significantly improve accuracy by considering context and tone.
By understanding the inner workings of sentiment analysis, businesses can unlock powerful insights to guide decisions and enhance customer satisfaction. Whether analyzing reviews, social media, or surveys, sentiment analysis turns unstructured text into a valuable resource for growth.
Learn how to use your affiliate network to gather customer feedback and keep affiliates engaged. Discover tips for surveys, the value of open-ended questions, and how Blix streamlines feedback analysis into actionable insights.
Verbatim coding is an essential process for analyzing qualitative data in market research, such as open-ended survey responses and textual customer feedback. Think of it like this — your surveys and customer reviews contain hidden treasures about how to improve your business, and the process of coding verbatim is like drawing the treasure map.
