15 Tips for Using Python to Research Instagram Posts & Comments

15 Tips for Using Python to Research Instagram Posts & Comments

Instagram, as revealed in discussions on the same over at runrex.com, is one of the most popular social networks out there, with over 1 billion active monthly users and 500 daily active users. This means is that while it is a great platform for brands to reach out to their target audience, it is also a great place for them to learn given the huge amounts of data on the platform. Through Python, you can be able to research Instagram posts and comments, allowing you to extract helpful insights from them, and to that effect, the following 10 tips are worth considering.

• The official API

As is discussed over at guttulus.com, Instagram has an official API that you can use to access data on Instagram. This official API will allow you to programmatically access useful data like comments and posts and can be accessed through Instagram’s Developers site, with the process involved being one that is pretty simple and straightforward.

• Limitations of the official API

One of the reasons why most developers don’t prefer using the official Instagram API is because it has several significant limitations. According to runrex.com these limitations includes the fact that it only allows you to access your own posts and not those by others, which limits what you can do and the data you can access and analyze on the platform.

• Unofficial APIs

To get around the many limitations that come with Instagram’s official API, some developers have taken to using unofficial APIs like the popular one by LevPasha which, as discussed over at guttulus.com, contains all the major features you would expect of an API without the limitations that come with the official one.

• Instagram scrapers

Another very popular option is the use of Instagram scrapers which allow you to access all the publicly available data that is not tied to your own account as explained over at runrex.com. If you are to make the most of the data that is there to be found on Instagram, then using these tools with Python is the way to go.

• Proxies

Given that Instagram has a very strong anti-bot system in place to prevent scraping and other forms of automatic access and traffic to the platform, an important tool to helping you sidestep this issue is the use of proxies. Mobile proxies are preferable given how good Instagram is at detecting proxies, although if they are too expensive for you, then residential proxies will do as well.

• Examples of the best Instagram scrapers

Although there are many Instagram scrapers available out there in the market, some are more impressive than others. As outlined over at guttulus.com, examples of Instagram scrapers to use include Octoparse, Jarvee, Apify Instagram Scraper, and ScrapeStorm, just to mention a few of them.

• Requests and BeautifulSoup

When scraping Instagram with Python, 2 packages come into sharp focus according to the subject matter experts over at runrex.com: Requests and BeautifulSoup. The former is needed to make HTTP Requests and the latter is needed to make HTML parsing more user-friendly. This is why you should have them installed, or downloaded in case you don’t have them available.

• Selenium

As is revealed over at guttulus.com, the Instagram web application was built heavily with JavaScript which means that you have a lot of AJAX and XHR requests to deal with. To, therefore, render and execute JavaScript, you will need Selenium which is a very important browser automation tool for any Python developer out there.

• Available data depends on whether you are logged in or not

From discussions over at runrex.com, there is particular data that is available publicly on Instagram and which you can access even without logging in, including profiles, posts, hashtags, comments, and places. On the other hand, if you want to access data like a list of followers and the list of people a user follows, then you will need to be logged in.

• Focus on the data available without logging in

When scraping Instagram with Python, it is recommended that you focus on the available data without requiring you to log in. This is because scraping Instagram while logged in will make it easy for its anti-bot system to discover you, which will lead to your IP being blacklisted and your account being banned.

• Create accounts for scraping work

If you want to be logged before scraping Instagram with Python, then one way to protect yourself is to create Instagram accounts that are specific for scraping work so that if they are sniffed out and banned, it spares your main account from getting banned. However, if you are considering this option, the gurus over at guttulus.com point out that you will need to be good at engineering your bot to evade the check activated on logged-in accounts and their activities.

• Scrape a bunch of Instagram accounts

One of the benefits of scraping Instagram with Python is that you can be able to scrape a bunch of Instagram accounts that are of interest to you, and not just one, as outlined over at runrex.com. This will allow you to scrape more effectively and efficiently, getting through it a lot quicker. To achieve this, you will need to use the argument -f.

• Downloading stories

When scraping Instagram with Python, there is a command that allows you to download the Instagram stories from a certain account, which includes both daily stories and highlights as discussed over at guttulus.com. However, it is worth pointing out that you can’t do this anonymously which means that you can only scrape stories if you are logged in.

• Sentiment analysis

Sentiment analysis, according to the experts over at runrex.com, will allow you to deduce how your account is perceived. This is because it will let you know whether certain content or accounts are perceived in a manner that is positive, negative, or neutral. When scraping Instagram with Python, this is something you have to ensure that you do.

• Downloading comments, number of likes, as well as number of comments

Other than just allowing you to download the images and videos of certain posts, certain Python queries will also give you additional information on these media such as the number of likes, number of comments, user ID, and even download the comments from the posts. This is yet another thing you should look to do to give you as much useful information as possible.

Remember, if you are looking for more information on this and other related topics, then look no further than the highly-rated runrex.com and guttulus.com.