How do you measure sentiment analysis ?

In a world where data is at the heart of every strategic decision, understanding what consumers think and feel has become indispensable. This is where sentiment analysis comes in, a powerful approach to decoding the emotions and opinions expressed in digital texts.

Whether on social networks, in customer reviews or surveys, this method is proving to be a major asset for companies. But how does it work, and what tools are available to measure it effectively ?

What is sentiment analysis ? 

Sentiment analysis, also known as opinion mining, is a technique derived from artificial intelligence and computational linguistics. It involves analyzing textual data to identify and categorize the emotions or opinions it conveys. These feelings can be positive, negative, neutral or nuanced.

The main aim is to provide companies with usable analyses to adapt their products, campaigns or services to users' actual expectations.

The Master Data Science & Business Analysis of EDC Paris Business School trains experts capable of transforming data into strategic levers for companies. The program combines technical skills in artificial intelligence and data analysis with a business vision, fostering informed, innovative decision-making.

How does sentiment analysis work ?

Sentiment analysis relies on sophisticated algorithms capable of examining large quantities of text in record time. Here are the key stages in its operation:

1.Data collection

Texts come from a variety of sources, including social networks, emails and forums.

2. Linguistic pre-processing

Algorithms clean up the data (removing unnecessary words, correcting errors, etc.) to make it usable.

3 Text analysis

Content is broken down into units such as words or phrases, then analyzed to detect emotional cues or specific opinions.

4 - Sentiment modeling

Machine learning or lexical models establish the polarity of emotions.

5 - Interpretation of results

Insights are transformed into clear reports to guide decision-making.

Types of sentiment analysis

Sentiment analysis falls into several categories, tailored to the specific needs of each company.

Polarity analysis

It can be used to determine whether a text expresses a positive, negative or neutral opinion. This method is particularly useful for measuring customer satisfaction or tracking changes in brand perception.

Emotional analysis

This analysis goes further, identifying complex emotions such as joy, anger or sadness. It helps to better understand users' reactions to a product or service.

Contextual analysis

It takes into account cultural nuances and the context in which the text was produced. It enables a finer, more precise interpretation of the feelings expressed.

Comparative analysis

With comparative analysis, it is possible to compare several brands, products or services according to the opinions expressed by consumers. This type of analysis is particularly popular in market research.

Real-time analysis

It offers the ability to monitor trends instantly. It is often used to react quickly to crises, or to monitor discussions on social networks.

Each type of sentiment analysis provides complementary information, enabling companies to better understand and anticipate the expectations of their audiences.

AI responsible for improving opinion mining

Deep learning models, such as neural networks, can now achieve impressive levels of accuracy. However, this technological advance also poses ethical challenges.

Responsible AI aims to ensure that these tools respect principles such as data confidentiality, elimination of bias and transparency in algorithmic decisions. These efforts are helping to strengthen business and consumer confidence in these technologies.

Tools for measuring sentiment analysis

TextBlob

An intuitive Python library, ideal for beginners.

VADER (Valence Aware Dictionary and Sentiment Reasoner)

Specialized in social network data analysis.

Google Cloud Natural Language API

Provides advanced analysis, including entity extraction and sentiment analysis.

Lexalytics

Integrated solutions for large companies.

IBM Watson Natural Language Understanding

Offers a complete suite for decoding emotions and opinions.

These tools differ in terms of accuracy, functionality and cost, but their common goal is to help companies better understand their audience.

Sentiment analysis is more than just a trend: it's a necessity for companies seeking to remain competitive in a data-driven environment. Thanks to advanced technologies and high-performance tools, this method captures the essence of the opinions expressed by users. However, its use must be accompanied by sound ethical practices to guarantee truly responsible artificial intelligence.