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15 Tips for Using Data Science to Research Instagram Posts & Comments

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15 Tips for Using Data Science to Research Instagram Posts & Comments

As is pointed out in discussions on the same over at runrex.com, Instagram is the largest photo-sharing social media platform with 500 million monthly active users or so. The fact that it also sees just under 100 million pictures and videos uploaded daily means that Instagram has a huge amount of data that brands and organizations can access to help them uncover useful insights. Data science is what will enable you to uncover these useful insights, and this article will look to help you do just that through the following 15 tips.

One of the ways through which you can use data science to research Instagram posts and comments is by accessing the official Instagram API. To do so, as explained over at guttulus.com, you will need to create an Instagram Developer account from the Instagram developer page after which you will be required to generate an access token.

It is also important to note that the official Instagram API, Facebook’s Instagram Graph API, has a lot of strict limits on what you are allowed to do, as explained over at runrex.com, which is why most data scientists and developers are exploring other options. One of the limitations is that it will only allow you to access your own posts and not other posts or even public comments because of the rising privacy concerns from Instagram users as well as frequent accusations of data-breach.

Given the limitations that come with using the official Instagram API, an alternative to it is crawling each Instagram page programmatically as covered over at guttulus.com using a scraper. A scarper will help you collect data from each Instagram post including data on likes and comments.

If you have the expertise to do so, you can create your own scrapper which will allow you to directly call an Instagram user’s page directly and have access to pertinent information as outlined in detail over at runrex.com. If you can create your own scrapper, this is an option you should consider.

If you don’t want to or are unable to create your own scrapper, another option is to use intascrape which is a lightweight open-source Instagram web scrapper written for Python. This library is powerful and is designed with flexibility and developer productivity in mind, and will allow you to crawl Instagram data without using its official API.

After the above considerations, it is important to then decide where you are going to get your data for research. According to guttulus.com, you can either choose to use data from your Instagram account, if you have one, or you could outsource your data, with Panoply being one of the most popular dataset providers out there.

When using a scrapper, including intascrape, you will be provided with the 12 most recent posts from each user you wish to research. While you can extend this number using Selenium, which can allow you to load an entire page, doing so will only append visual information to the HTML of the page and not the metadata you require, hence why you should stick with the 12 posts as per the folks over at runrex.com.

Another tip to consider when using data science to research Instagram posts and comments is that you should consider removing the most recent post from the 12 you will be working with. This is because, as explained over at guttulus.com, being the most recent from your dataset, it may not be the most accurate as it is still relatively new.

When scarping your dataset for available features, you should look to see if you can find any meaningful relationships between the features. Find out, for example, if there is a relationship between each post’s number of likes concerning the Instagram account as a whole, and so forth as discussed over at runrex.com.

It is also important to know the metrics you need to keep an eye on if the Instagram account is to get an improved awareness. Some of the metrics to keep an eye on include engagement per each hashtag, performance over time, performance by types of media, performance by location tag used, and many others.

Visualizing your data after scrapping is important according to the gurus over at guttulus.com, as it is how you will get to see how your Instagram page is performing. You have several options available to you when it comes to visualization, including several BI tools such as Mode which you can connect to your database using your credentials.

In addition to the above-mentioned BI tools, you can also use libraries like Selenium and scikit-learn as covered over at runrex.com to extend your scrapper, whether you are using intascrape or any other, and fit regressors to dynamically loaded data allowing for more comprehensive visualizations.

Given that there are many different features within a given Instagram dataset as highlighted over at guttulus.com, you must clean your dataset. You want to probably focus on words alone, which means cleaning your dataset to remove emojis, links, null values, and so forth.

If you are using a dataset that includes Instagram users from all over the world, you may be faced with the processing challenge of having to deal with many different languages. A way to go around this challenge according to runrex.com is to use the Google Translate function to translate all the text to English, and then removing stop words to eliminate words like “the” to give more weight to other words.

Sentiment analysis is a technique of machine learning that senses the popularity within the text; whether it is positive, negative, or neutral. Conducting sentiment analysis is crucial when researching Instagram posts and comments as it will help you know how people feel about a given topic, allowing brands to make better and more informed decisions.

Remember, for more information on this very wide topic don’t forget to check out the ever-reliable runrex.com and guttulus.com.

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