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Question and Answer websites like Quora provide online socialization centers for digital people worldwide to question, answer, and examine the most projecting issues, topics, and doubts. Scraping big-scale data from online Question and Answer platforms could be helpful to data scientists and marketers as this is a multilingual Question and Answer website with social networking with different niche influencers. Let's understand how to extract Questions & Answers data from Quora using Python and BeautifulSoup.
To highlight why Quora data scraping is of great interest to businesses and marketers, let's take a quick look at some critical Quora statistics:
1. Sentiment Analysis
You can extract questions associated to stock market, politics, brands, etc. to do sentiment analysis.
2. Machine Learning & NLP
The majority of users on Quora are all real users that ask questions or answers on this platform in the daily lingo. This might be extremely useful to train ML models, and NLP (Natural Language Processing).
3. Intelligent Influencer Marketing
Quora helps you run ads however, you can target influencers within a specific niche to endorse your brand. Extracting user profiles, questions, etc. from any precise niche might help you partner with right influencers that have the real authority of promoting your brand.
4. Lead Generation and Content Marketing
Different questions asked by the users can assist you recognize in case; they are your targeted leads. For example, if you’re any IT service company then people that ask questions including “How much that cost to make an e-commerce site?” are your future leads. Insights added from extracting Quora Q&As might also be your gateway of having an astral content marketing strategy.
We shall use Python3.7 and a BeautifulSoup library to scrape Quora data and save that in the JSON file. This code makes it easy to scrape Quora questions and answers easily. Also, you will require a good text editor. We have used PyCharm, a full-blown IDE; however, you may also utilize Atom, as it comes with different plugins.
So, to begin with, a code, we have started importing libraries, which we would need, both external and internal. When done, we have to ensure that we set a verify mode of the SSL certificate to "CERT_NONE" and select the hostname to 'False,' to avoid all the SSL certificate errors while scraping data. When this is done, the setup is completed, and we accept the questions from the users. For the demo, we gave the following value when the question was asked.
We make the Quora URL of this question. The string manipulation is needed as Quora formats the URLs in that manner.
When we create the URL, we utilize the in-built Request function through urllib for hitting a page and ensure that we add Firefox within the header so that a website can't track that we are using it from the piece of code. This part is vital as most sites block web scrapers, and in case you miss any header, the IP will get blocked, and more actions could be started against you.
After we obtain a page in the HTML format and store that in the variable. We have to convert that into a BeautifulSoup object, and it will be easier to extract and parse data from. After that, scrape a question on a webpage from the initial "title" tag on a page. We must remove "– Quora" from that, as all the titles are available with the given string. Extracting the answer is a bit more complicated. You have to scrape the JSON saves in the element type "script," getting the value for the "type" as "application/ld+json." When you obtain the JSON, you shall get the listing of answers having different fields. A few areas are provided for every solution; we have scraped the most significant ones:
When the data scraping is done, we can add it to the answer list and save the final listing in the JSON file.
A JSON file provided here has a few answers extracted from the HTML pages when we ran a code with questions given in the last section. As the real answers extracted for the particular questions were numerous, we have only provided some of them here. The JSON comes here with two fields, questions and solutions. Every answer includes the three parameters mentioned earlier.
While it might seem an ideal solution to finding answers to all the questions on Quora, like all other pieces of DIY codes, it has many limitations. One vital aspect is not all the questions you type would occur in Quora. You would have the code break whenever you type any question which doesn't exist. At the same time, you may have to type questions multiple times to know which question exists. A superior implementation might be to get a question that matches the one you had entered closest.
Another feature to study is the one related to the worries of extracting Quora data and how you select to utilize it. You have to use the robot.txt file to extract data and use that accordingly. Any profitable use of the code may lead to legal problems. And utilizing the collected data for everything other than research objectives might also source problems.
Social media is the goldmine for user-produced data. Extracting Quora Q&A data is like getting access to all the customers' pain points, including the audience's likes, dislikes, or interests. Using any intelligent scraping tool will solve all your pain points related to extracting Quora data. When you scrape your data, you could run neural networks-powered ML algorithms to get business-important insights.
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