Basics of Sentiment Analysis

With the abundance of information available on the Internet, it can be overwhelming for consumers to make informed decisions about purchases. We use sentiment analysis to identify and summarize common points of feedback that consumers and experts have about particular products in online reviews.

What is Sentiment Analysis?

Sentiment analysis is a technique that uses Artificial Intelligence / machine learning algorithms to examine and categorize emotions and opinions expressed in text data.
This data can be reviews of products on e-commerce websites, social media posts or reviews/videos about particular brands or products.

Sentiment Analysis allows consumers to gain valuable insights about the product's common strengths and weaknesses without having to read dozens of reviews.

Using Sentiment Analysis for Product Research

ReviewFinder utilizes sentiment analysis to provide consumers with valuable insights about products. By summarizing the most valuable customer and expert reviews, ReviewFinder is able to identify the frequently mentioned strengths and weaknesses of a product. The use of sentiment analysis allows ReviewFinder to analyze the tone of reviews and identify the key points that customers and experts mention about the product.

For example, if a person is looking for a new smartphone, ReviewFinder can provide a summary of the most valuable reviews for different models. By analyzing the sentiment of the reviews, ReviewFinder can identify the most frequently mentioned strengths and weaknesses of each phone model, such as battery life, display quality, and performance.

This approach saves consumers valuable time by providing them with a concise and informative summary of the product's most important features. In addition, the use of sentiment analysis allows ReviewFinder to take into account the opinions of both customers and experts.