Blog / Learn how to analyze sentiment in Yelp restaurant reviews effectively. Discover the techniques and tools to gain valuable insights and make data-driven decisions for your restaurant business.
12 July 2023
Sentiment analysis is the process of measuring and categorizing whether a piece of writing is positive or negative. It's a method that has been around for decades but has recently become more accessible and more popular due to the growth of sentiment analysis apps. This post details how you might use sentiment analysis to help you decide whether or not a restaurant review on Yelp is worth reading by giving an example application.
Sentiment analysis can be used in various ways. Still, it typically analyses reviews on websites Yelp restaurant reviews (and similar sites), where users write reviews about anything, from restaurants to books.
You should analyze sentiment to determine whether a Yelp review about a restaurant is positive or negative. Site users can write very detailed reviews, and this kind of feedback can be helpful when deciding where to eat. The reviews are often funny and entertaining, but some are more trustworthy than others.
One way to identify a trustworthy review from an untrustworthy one is through sentiment analysis. This type of analysis gives the likelihood that the review is positive or negative. Thus, you can identify unreliable reviews which only provide a little information.
One use case for sentiment analysis is when you are following up on a potential client. You want to find out if they liked the company you work for or if they were speaking in the first person so that you can decide whether or not to keep working with them.
Sentiment analysis is also useful when looking at feedback about your product or service and helping determine how the product/service should be improved.
A positive review is more likely to be written by someone who enjoyed the experience. It means that the reviewer had a good time and was happy with the outcome of their visit. These are only sometimes positive experiences, but this is what you're looking for in our application. You may want to know whether it's better to have a negative experience or some valuable criticism that should be noted.
The sentiment analysis process will be used at two levels. First, we'll look at each question within a review and determine if the response is more likely negative or positive than neutral.
Second, we'll analyze the complete review to decide whether it's more likely positive or negative.
Also, while sentiment analysis can detect positive reviews, there is no way of knowing when a negative review is realistic or a joke. For example, you would want to avoid launching your product based on an earlier case where the customer found a bug in your product and wrote about how their laptop was on fire. This is an exaggeration, but it wouldn't be clear because the reviewer wasn't writing in "the first person." There would be no way of knowing they were speaking genuinely and not exaggerating the situation.
This is why it's better to look for broader strokes, such as whether the review is positive or negative. Combining these two methods will give you a result you can trust.
First, we will use Reviewgator to scrape reviews from Yelp. Then, we will use a Python script to perform sentiment analysis on these reviews.
With Reviewgator scraping services, you can create a database of reviews, which you can edit based on your needs. You can add fields or remove them from the results as well. Reviewgator is also a helpful website that allows you to generate random data. Contact us to get the best reviews scraping services.
We used a Python script to perform the sentiment analysis. You can use various libraries to perform sentiment analysis, such as the VADER framework, which is what we used. VADER stands for Valence Aware Dictionary and sEntiment Reasoner. It's a toolkit written by Vladimir Minkov that is easy to use and understand, even for beginners or programmers who have not worked with natural language processing.
Before you can start working with the script, you should install all of the required packages:
python -m pip install -r requirements.txt
python -m pip install -r requirements-dev.
This will install all of the required packages. Notice that you need to install the requirement "requirements_dev" as well. If you don't, the script will not run.
The code we used to perform sentiment analysis on Yelp reviews is in the yelp_sentiment.py file. All the information you need is included in this file and the comments.
Let's see how you can use this script:
python yelp_sentiment.py --usage
python yelp_sentiment.py --help #get command usage help #python yelp_sentiment.py --help: same as long option
The script will start running and give you the results of the analysis. Here are a few samples of what you will see:
NegativeThe people here are always lovely, and the service is fast. I have never waited more than 10 mins to order or get my food. My only complaint is that sometimes it can be hard to find a place to sit during peak times such as lunch or dinner. However, there are plenty of tables during breakfast hours.
NegativeThe service is horrendous. I ordered cheese sticks and waited an hour for them. So bad I walked out without eating.
positiveI love this place! It's small and quaint, but they serve delicious food. It's not the most expensive place I've ever been to, but it's close. The salad bar is impressive, and there is a variety of dressing.
PositiveGood food at a reasonable price, great daily specials! Their lunch menu has many selections for under $10, and their dinner menu is also very affordable, with several choices around $10 or less (though a few of the pricier ones are good too).
PositiveIf you are looking for healthy meals, this is the place to go. I've had the falafel, and it's good. The portabello sandwich with pesto and roasted potatoes is also delicious. The atmosphere is very relaxing.
There are two critical areas of improvement that can be made by using sentiment analysis in your restaurant reviews:
Get reviews from consumers who are more likely, to be honest, and more likely to give you a fair review. Getting the right customers at the right time provides an excellent opportunity for your restaurant.
If you want a higher-quality set of customers, you should focus on posting content that appeals more to them (for example, markups in specific categories). If you're targeting satisfied customers, it makes sense for you to focus on providing better service. It's always better to use data than just gut feelings.
Using sentiment analysis for restaurant reviews, you can see what time of the day or days of the week you should put up your ads. This is useful because you want to capitalize on a more significant number of customers during the best times.
If you already have a restaurant, use sentiment analysis to get more reviews from those more likely. For example, your restaurant caters to older people (and they may not use Yelp as much). Sentiment analysis will allow you to focus more on getting reviews from younger people who are more likely to review your business via platforms like Yelp and Google.
Overall, sentiment analysis can help restaurants improve their businesses by getting more and better customer feedback. The collection and analysis of those who are more likely, to be honest, is essential for any business that serves customers. This can be done through the use of a few simple scripts.
Sentiment analysis can provide excellent insight into how your restaurant is performing. If you want to improve areas like efficiency or customer service, then sentiment analysis is a great way to get started.