Have you ever wondered how companies know exactly what you think about them? While they can’t read minds, they can analyze your tweets, emails, reviews, and pretty much everything else you write online. This is where sentiment analysis comes in.

What is Sentiment Analysis?

Sentiment analysis involves analyzing large volumes of text to determine the sentiment expressed — whether it’s positive, negative, or somewhere in between. It helps companies understand their customers better, deliver stronger customer experiences, and improve their brand reputation. However, it’s not without its pitfalls.

The Basics of Sentiment Analysis

Sentiment analysis is built on Natural Language Processing (NLP), which trains software to analyze and interpret text in a way that mimics human understanding. There are a couple of main approaches to this: rule-based and machine learning-based, and sometimes a hybrid of the two.

Rule-Based Approach

In the rule-based approach, the software is trained to classify certain keywords in a text-based on groups of words called “lexicons.” Lexicons are groupings of words that describe the author’s intent. For example:

– Positive Lexicons: affordable, well-made, fast
– Negative Lexicons: expensive, poorly made, slow

The software scans the text for these keywords and calculates a sentiment score based on their frequency and context. For instance, a review saying, “these shoes are affordable, well made, and shipping was fast” would score highly in the positive lexicon and be considered an overall positive sentiment.

However, the rule-based approach can struggle with nuances in human language, such as sarcasm and negation. For example, a sarcastic review saying “Oh, wonderful. A pair of shoes so well-made they lasted me one full week.” might be misclassified as positive because of words like “wonderful” and “well-made.”

Machine Learning Approach

The machine learning approach tackles some of these issues by training algorithms on large datasets to recognize patterns, including the complexities of human language. Developers use sentiment analysis algorithms to teach software how to identify emotion in text in a way that is similar to how humans do. This is typically done using classification algorithms such as:

– Linear Regression: Predicts a sentiment score based on various features in the text, such as the frequency of positive and negative words, review length, and specific emotive phrases.
– Naive Bayes: Uses Bayes’ theorem to classify text by calculating sentiment probability based on word occurrences. It determines the likelihood that a new review is positive or negative based on a labeled dataset of reviews.
– Support Vector Machines (SVM): Identifies the optimal boundary that separates positive and negative reviews by analyzing features like word frequencies and phrases, ensuring the maximum margin between the two groups.

Together, these approaches help handle sarcasm, negation, and idiomatic language in the human-generated text.

Types of Sentiment Analysis

Organizations can use various types of sentiment analysis to get a clearer picture of customer sentiments, depending on their needs. All these types rely on the software’s ability to gauge something known as polarity, which is the overall feeling conveyed by a piece of text.

Fine-Grained Sentiment Analysis

Also known as “graded” sentiment analysis, this type groups text into different emotions and levels of emotion. Polarity is often expressed as a numerical rating on a scale of 0 to 100, where zero is neutral, and 100 represents the most extreme sentiment.

Aspect-Based Sentiment Analysis (ABSA)

ABSA narrows the focus to specific aspects of a product, service, or customer experience. For example, a budget travel app might use ABSA to analyze user feedback specifically about its new customer chatbot. This helps businesses understand precisely what customers like or dislike about particular features, allowing them to address those specific issues.

Emotion Detection

Emotion detection seeks to understand the psychological state of the individual behind the text, including their frame of mind and intentions. It’s more complex than fine-grained or ABSA and is typically used better to understand a person’s motivation or emotional state. For example, a support ticket saying, “I’m extremely frustrated by the repeated issues,” reveals not just negative sentiment but also the specific emotion of frustration.

The Core Idea

By understanding sentiment as it relates to a customer’s overall experience, specific features, and underlying emotions, an organization is empowered to make meaningful changes. They can learn which issues to escalate in a support forum or conduct market research on competitors to spot trends and identify new opportunities.

Ultimately, sentiment analysis is a tool to extract meaningful insights to guide business decisions. When done right, it can navigate the nuances of human language, turning even the trickiest reviews — yes, even the most sarcastic ones — into clear insights.

Conclusion

Sentiment analysis is a powerful tool that enables companies to understand better and respond to customer feedback. By leveraging rule-based and machine learning approaches, organizations can gain a deeper understanding of customer sentiment, helping them make informed decisions and improve their offerings. If you have any questions, please drop a line below.