How to Scrape TripAdvisor Reviews Using Python?

In the vast realm of online reviews, TripAdvisor is a prominent platform where travelers share their experiences, opinions, and recommendations. Web scraping is a powerful technique for extracting information from websites such as TripAdvisor and assisting corporations, researchers, and enthusiasts in gathering data for study or businesses; it's a cheat sheet on what customers like or dislike. They can see what they're doing well and where they can improve.

Researchers use web scraping to study trends and patterns. They look at the big picture of what people enjoy or avoid in travel, helping them understand how things change over time. For regular dwellers planning a trip, web scraping helps by creating a personalized guide based on what others have experienced.

What is TripAdvisor Reviews Scraping?

TripAdvisor review scraping means getting information from the TripAdvisor web page. TripAdvisor is a website where people share their thoughts and ratings about different places like restaurants and hotels. Web scraping helps us collect a lot of this information quickly.

Assume you want to know what people on TripAdvisor think about a restaurant. Instead of reading each review individually, web scraping allows you to collect all the reviews automatically. The Tripadvisor Content API allows users to access information on accommodations, restaurants, and attractions, including:

• Reviews Links
• Ratings and Awards
• Accommodation Categories
• Attraction types & Location data
• Restaurant Cuisine
• Pricing and Availability

Why Scrape TripAdvisor Reviews?

Scraping TripAdvisor reviews can provide a wealth of valuable information for various purposes. Here are detailed reasons why someone might want to scrape TripAdvisor reviews:

Market Analysis

Understanding reviews helps companies know what their customers like and don't like about what they offer. With this information, businesses can make changes to make sure they're giving customers what they want and need. Keeping an eye on what other similar businesses are getting in their reviews is like having a unique advantage. This helps businesses know what they're doing well and what areas they can work on, helping them make intelligent decisions.

Reputation Management

Businesses can use scraped reviews to identify repeating positive or negative tendencies. Addressing complaints raised by customers can help a company's reputation. Prompt responses to positive and negative client comments add to a positive online reputation. Scraping assists in monitoring and responding to evaluations as soon as possible.

Improve Products and Services

Reading what customers desire allows firms to better their products and services. For example, if a restaurant is consistently appraised for its handmade sauce, they may wish to include it in more dishes. Positive reviews might be utilized in advertisements or on the company's website to attract additional clients. For example, a tour business may utilize a positive review from a customer in their brochure to demonstrate to others how much people love their excursions.

Examine Customer Satisfaction

Checking star ratings quickly indicates how much customers like a location. For example, if a museum consistently receives five stars, it suggests that the majority of tourists appreciate it.

Make Sound Decisions for the Future

By looking at reviews over time, businesses can see what's working and what's not. For Example, If a theme park notices a drop in positive reviews after changing a ride, they might rethink that change. Reviews can reveal interesting trends in what people like or dislike. For Example, If many reviews mention a new dessert at a café, the owner knows it's becoming popular.

Steps to Scrape TripAdvisor Reviews

In web development and data analysis, it is common practice to scrape data from websites.

We will discover a step-by-step method for utilizing Python to scrape TripAdvisor. The BeautifulSoup and requests libraries will be utilized to extract TripAdvisor reviews.

Prepare the Setup

Download the most recent version of Python is available online.

Install Dependencies
Install Python libraries relevant to scraping. Execute the instruction indicated below.

pip install bs4 requests pandas

It will automatically download and install Beautiful Soup, Requests, and Pandas.

Import required libraries

Import the required libraries for usage in a subsequent step.

from bs4 import BeautifulSoup
import requests
import pandas as pd

Get the API credentials

Utilise the Tripadvisor API to scrape data. After signing up, you will obtain your API credentials. You can save it as a tuple, as seen below.

credentials = ('USERNAME', 'PASSWORD')

Prepare the payload

Get your payload ready for an API POST request. Tripadvisor need a global source. Additionally, you must set render to html.

url  = "https://www.tripadvisor.com/Search?searchSessionId=000a97712c5c1aad.ssid&searchNearby=false&ssrc=e&q=Nearby&sid=6786CB884ED642F4A91E6E9AD932BE131695517577013&blockRedirect=true&geo=1&rf=1"
payload = {
    'source': 'universal',
    'render': 'html',
    'url': url,
}

Just use your search query instead of the provided URL.

Send a POST request

To send the POST request, together with your credentials and payload, to the chosen API. The payload dictionary is converted into a JSON object by the Requests module and sent to the API.

response = requests.post(
    'https://realtime.oxylabs.io/v1/queries',
    auth=credentials,
    json=payload,
)
print(response.status_code)

Scrape data

The response is sent by the API in JSON format. The following is how you can extract the page's HTML content.

content = response.json()["results"][0]["content"]
soup = BeautifulSoup(content, "html.parser")

Parsed HTML text will be present in the soup object. Selectors in CSS can be used to select particular items.

Let's gather the information shown below from the Restaurants category.

Name

In order to retrieve the name of a restaurant, you must locate the appropriate CSS selector first. Utilize the developer tools in your web browser to examine and locate the required CSS selector. Go to the webpage, do a right-click, and choose Inspect.

Examining a name will reveal that it is included in <span> within a <div> that has the result-title class. You may create the Beautiful Soup pickers with this information.

name = soup.find('div', {"class": "result-title"}).find('span').get_text(strip=True)

Rating

Examine the rating bubbles in a similar manner for rating.

The element has the class ui_bubble_rating, as you observe, and the rating is accessible through the alt attribute. To retrieve the alt attribute, use the find() function.

rating = soup.find('span', {"class": "ui_bubble_rating"})['alt']

Reviews

The class review_count can be used to extract reviews from the tag, as seen below.

This is how the code will appear.

review = soup.find('a', {"class": "review_count"}).get_text(strip=True)

NOTE: The find() method only retrieves elements from the first search result in each of the three scenarios. For information on obtaining all results, see the next section.

Search Results

Grab each result, then execute a loop to extract every result from the search. Find the CSS selector for each result that is enclosed in a

along with the class result first.

Update the code now to retrieve every search result.

data = []
for div in soup.find_all("div", {"class": "result"}):
    name = div.find('div', {"class": "result-title"}).find('span').get_text(strip=True)
    rating = div.find('span', {"class": "ui_bubble_rating"})['alt']

    review = div.find('a', {"class": "review_count"}).get_text(strip=True)
    data.append({
        "name": name,
        "rating": rating,
        "review": review,
    })

All of the search results are extracted by the code above and are kept in the data list.

Save to CSV format

Finally, utilize the to_csv() method in Pandas to export data to a CSV file.

df = pd.DataFrame(data)
df.to_csv("search_results.csv", index=False)

Full Code at a Glance:

from bs4 import BeautifulSoup
import requests
import pandas as pd

credentials = ('USERNAME', 'PASSWORD')
url  = "https://www.tripadvisor.com/Search?searchSessionId=000a97712c5c1aad.ssid&searchNearby=false&ssrc=e&q=Nearby&sid=6786CB884ED642F4A91E6E9AD932BE131695517577013&blockRedirect=true&geo=1&rf=1"
payload = {
    'source': 'universal',
    'render': 'html',
    'url': url,
}
response = requests.post(
    'https://realtime.oxylabs.io/v1/queries',
    auth=credentials,
    json=payload,
)
print(response.status_code)

content = response.json()["results"][0]["content"]
soup = BeautifulSoup(content, "html.parser")

data = []
for div in soup.find_all("div", {"class": "result"}):
    name = div.find('div', {"class": "result-title"}).find('span').get_text(strip=True)
    rating = div.find('span', {"class": "ui_bubble_rating"})['alt']

    review = div.find('a', {"class": "review_count"}).get_text(strip=True)
    data.append({
        "name": name,
        "rating": rating,
        "review": review,
    })

df = pd.DataFrame(data)
df.to_csv("search_results.csv", index=False)

Benefits of scraping TripAdvisor reviews

Extract TripAdvisor reviews offers several benefits to the travel industry that assist in data-driven decisions:

Improved Consumer Engagement

Recognizing and reacting to positive or destructive feedback displays a dedication to customer involvement. This connection has the potential to increase client loyalty and trust.

Efficient Travel Planning

Individuals planning travels can plan their journeys more efficiently by leveraging scraped reviews to make informed judgments on lodgings, destinations, and activities, resulting in a more fulfilling travel experience.

Spot Trends

Researchers can analyze tons of reviews to find common likes and dislikes. For instance, noticing a trend that many people prefer eco-friendly hotels can help the travel industry go green to meet customer demands.

Manage Reputation

Keeping an eye on reviews helps businesses know what people are saying. If there's a recurring complaint about slow service, a business can address it, keeping customers happy and maintaining a good online image.

Improve Products and Services

Businesses can learn much from what customers say about their products or services. If a tech company sees users consistently praising a feature, they know it's a hit. On the flip side, if there are complaints, it signals areas for improvement.

Get Personalized Suggestions

Travelers can use reviews to plan their trips better. If someone loves hiking, reading about other hikers' experiences in reviews can lead to personalized recommendations for scenic trails or outdoor activities.

Helpful Content for Marketing

Businesses can use positive reviews and standout customer experiences in their marketing. Sharing a review about a fantastic meal or a memorable hotel stay can attract new customers looking for similar experiences.

Make Smart Decisions with Data

Instead of guessing what customers want, businesses and researchers can use review insights to make informed decisions. If a bookstore owner sees a trend of customers asking for more mystery novels, they can adjust their inventory accordingly.

Conclusion

Unlocking the treasure trove of insights within TripAdvisor reviews through web scraping is similar to gaining access to a goldmine of valuable information. Whether you're a business looking to understand your customers better or a traveler planning your next adventure, using web scraping can give you a significant advantage.

For travelers, web scraping TripAdvisor reviews is a handy tool for planning trips. This way, you can choose where to stay, what places to visit, and what activities to try. It's all about making your travel experience unique and tailored by ReviewGators just for you.

Extracting TripAdvisor reviews is a powerful tool businesses and individuals can use to get ahead and make better decisions. The key lies in harnessing this capability responsibly and ethically to create a win-win scenario for all stakeholders involved.