Sentiment Analysis is an AI technique used to gauge public opinion or mood based on written or spoken language. It can quickly monitor and analyze large volumes of text, helping to understand sentiments towards products, opinions on social media, or customer satisfaction in reviews.


Imagine going through thousands of online reviews for your favorite toy store. It would be really tough, right? Now imagine you have a magic lens that immediately tells you whether each review is happy, unhappy, or neutral. That’s what sentiment analysis does. It helps us understand feelings from written words.

In-depth explanation

Sentiment analysis, also referred to as opinion mining, is an advanced AI technique often used in Natural Language Processing (NLP). It’s a process used to identify and categorize opinions or sentiments in a result set of data. Sentiment analysis can determine whether the writer’s attitude towards a particular topic or product is positive, negative, or neutral.

Sentiment analysis models are typically trained on text data and apply concepts from linguistics and machine learning. These models ingest data, typically text, and output a sentiment score. This score quantifies the polarity of the sentiment. Some techniques apply more complex measurements considering levels of positivity or negativity and even a range of emotions like happiness, frustration, anger, or sadness.

The techniques to build sentiment analysis models vary. A common approach is using supervised learning where the model is trained on a labeled dataset. Labels often denote the sentiment, such as positive, negative, or neutral. The model learns to associate these labels with the features in the text like specific words or phrases.

Another approach are lexicon-based methods that involve creating a lexicon or dictionary of words where each word is associated with a sentiment score. If a sentence contains many words with high positive scores, it’s likely expressing a positive sentiment.

Opinion mining is an important subtask in sentiment analysis, focusing on extracting people’s subjective information from the source text. Further advancements in sentiment analysis move to aspect-based sentiment analysis, identifying the sentiment towards specific aspects or features of a product or topic.

In practice, sentiment analysis is used in various domains like marketing, customer service, and public opinion measurement on political or social events. For example, an online retailer might use sentiment analysis to track customer reviews on their website, in emails, and on social media platforms. They could identify products or services that are causing customer dissatisfaction and respond promptly with improvements.

Natural Language Processing (NLP), Supervised Learning, Unsupervised Learning, Machine Learning (ML),, Text Mining, Opinion Mining, Aspect-Based Sentiment Analysis, Information Extraction, Deep Learning, Lexicon