Most small business product review websites out there use editorial and/or user-submitted ratings for scoring companies. These methods can result in ratings that are biased and skewed.
We take a different approach to reviewing small business products and services.
Every day, millions of people share their opinions on Twitter. They share their reviews about companies that offer small business tools, software, and products for running or growing a website
We monitor and use these micro-reviews and apply sentiment analysis to them to discover the positive and negative sentiments about those reviews.
In other words, we give you reviews that are powered by real people’s opinions on Twitter.
We like to think that you care about what other people think about a product, the same product that you are in the process of researching and evaluating.
What others think may help you decide whether or not you should purchase the product, tool, or software.
We don’t review everything.
We only review companies that offer tools, products, and software for starting or running a small business website or online shop because we know what SMBs need to get started online.
Why? Because we are a small business, too.
We want to help other small businesses out there because we are a small business, and we use or have used many of these tools, products, and software ourselves.
Review Squirrel takes a different approach to rating small business products and services. Every day millions of people share their opinions on Twitter about companies. We use these micro-reviews and we apply sentiment analysis to discover the positive and negative sentiment about these opinions. We give you genuine reviews that are powered by real people opinions.
How It Works
Our ratings are calculated from real people, and the approach we take for scoring small business tools and software is actually quite simple.
We monitor companies on Twitter and what people say (i.e., tweet) about these companies.
We apply a sentiment analysis algorithm to the public tweets, and this algorithm determines if the tweet is positive or negative (or neutral, which we exclude).
This allows us to work out a score (an approval rating, if you like) of how many people like or dislike a company.
We use this simple formula: positive tweets divided by positive tweets plus negative tweets (# Total Positive Tweets / (# Total Positive Tweets + # Total Negative Tweets).
Check out our Frequently Asked Questions section to learn more about what sentiment analysis is, the tweets and scores that we display, and more.
Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis, and computational linguistics to identify and extract subjective information in source materials. Sentiment analysis is widely applied to reviews and social media for a variety of applications, ranging from marketing to customer service Wikipedia
Frequently Asked Questions
What is Review Squirrel?
Review Squirrel is an independent review website founded in 2015. We review the best tools, software, and products for starting or growing a small business website or online shop. Find out more about us here.
How is Review Squirrel different from other product review websites?
Unlike most other product review sites, we use sentiment analysis algorithms on real people’s opinions to rate companies. Find out more about us here.
What is sentiment analysis?
Sentiment analysis (or opinion mining) is the automated process of using machine learning to understand if a text is positive, neutral, or negative.
What is machine learning?
Machine learning is a discipline within artificial intelligence (AI) that builds algorithms that allow computers to learn to perform tasks from data themselves instead of being explicitly programmed to perform tasks.
I see tweets that have the wrong classification, such as negative tweets that are labelled as positive and vice versa. Why is that?
Sentiment analysis is a very complex machine-learning task. Not only do algorithms have problems predicting the sentiment of a tweet but humans also have a rough time agreeing on the sentiment behind a tweet or any given text.
Performing sentiment analysis on tweets is tricky. It’s one of the most complex machine-learning tasks out there. Tweets are short texts of around 140 characters, and things like sarcasm, poor spelling, lack of context, and the subtleties of sentiment make it difficult for a machine-learning algorithm to understand the real sentiment behind a text’s expression.
Aren’t people on Twitter more likely to express negative opinions? Won’t this skew ratings?
It’s true that people on Twitter are more likely to tweet dissatisfaction than satisfaction. This makes the overall ratings generally low, but comparison is key here.
Our ratings shouldn’t be taken out of context; they only make sense on our site in comparison to other companies.
The ratings should be based on their ratio of positive to negative reviews. This provides a good idea of which companies are good and which companies are not so good.
I can see one of my tweets. Can you remove it?
Absolutely! If you wish for it to be removed, please contact us. Please note that we may need you to verify your identity (we need to make sure that you are you!) before we can remove the tweet.
How can I contact Review Squirrel?
If you have any feedback, questions, praise, or concerns, don’t hesitate to contact us. We would love to hear from you.
Review Squirrel is an independent review website. We review the best products, services, and software for running or growing your small business website or online shop, and we apply a sentiment analysis algorithm to score companies so that you can compare and find the best company for your needs.
Who We Are
Review Squirrel is an independent review website founded in 2015 that reviews and compares the best products, services, and software for running or growing your small business website or online shop.
The team behind Review Squirrel consists of a group of review-gathering nuts. Matt is the head squirrel. He comes from an online marketing background, and his job is to make sure the site is up and running. He also promotes the site, works with partners, and does loads of other things.
Then, there is Suzy, who comes from a project management background, and she is in charge of the more administrative tasks. Finally, we have Shermaine and Laurence, who write and look after the content and the site’s social media presence.