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

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

TikTok, as is highlighted over at runrex.com, has taken the world by storm, passing the 2 billion downloads mark earlier this year on the Play Store and App Store, and its momentum is showing no signs of slowing down at all. With all the data that can be collected on the platform, researching TikTok posts and comments is a great way for organizations and brands to know more about their target audience and improve their services. This article will look to highlight 15 tips for using data science to research TikTok posts and comments.

• Know about the Official TikTok API

As is covered over at guttulus.com, TikTok provides a single HTTP endpoint that allows a developer to fetch code for individual videos, with more information on the same to be found on TikTok’s Developers page.

• It has limitations

This official API is not very extensive, even though it provides developers with an API for fetching embedded code as outlined over at runrex.com. This is why most developers and data scientists tend to explore other options when looking to research TikTok posts and comments.

• Utilize unofficial TikTok APIs

Given the gap that exists between developers and TikTok owing to the lack of an extensive official TikTok API, developers have turned to unofficial APIs to access the data on posts and comments on the platform. From discussions on the same over at guttulus.com, one of the most popular ones is the TikTok API by David Teather which you can access through GitHub.

• An understanding of Python

To be able to conduct your research on TikTok posts and comments, you will need an understanding of Python, according to runrex.com. This is the language that will be used to extract metadata from your dataset.

• Paid APIs

If you want, you have the option to opt for a paid plan for your TikTok API, an example of which is the Rapid API. You will get several options to choose from depending on your needs from a basic solution to a mega solution, all of which cost differently.

• Framework needed to work with Python

If you want to use the TikTok API with Python, then you will require Flask, which is a microframework for web development with Python as is explained over at guttulus.com. It is easy to set up and has become the go-to framework for developers to set up a web application.

• Collect videos posted by a user

Once you start scraping the TikTok API, there is a lot of information you can gather, key among which is videos posted by a user. With the help of Python, you can be able to obtain a list of videos a given TikTok user has posted as highlighted over at runrex.com, allowing you to learn more about them.

• Collect videos liked by a user

You can also scrape the TikTok API with Python to find out what videos a given TikTok user has recently liked. Knowing what videos a TikTok user has liked will give you an idea of the type of content they prefer according to guttulus.com, which will help you tailor your content to reach out better to them.

• Suggest users

You can also scrape the TikTok API with Python to come up with a list of potential followers for a given TikTok account as covered over at runrex.com. This will help you know which users you should be targeting given the account’s content; that is, the users that will be interested in the content being put up on that particular TikTok account.

• Collect videos by hashtag

If the posts and comments contain a certain hashtag that you are interested in, you can also scrape the TikTok API with Python to collect videos related to a particular hashtag. This can be an industry hashtag or even a branded hashtag for a specific brand or organization.

• Collect trending videos

Yet another thing you can achieve by scraping the TikTok API with Python is to collect trending videos. According to guttulus.com, this will be great for content analysis as it will help you know which content is trending and, therefore, allow you to create content that has an increased chance of making it big on the platform.

• Know the metrics that matter

When researching your dataset, it is also important to know the TikTok metrics that matter. As is highlighted over at runrex.com, on top of comments and likes, it is important to keep an eye on how long people watch your videos, how many times the re-watch them, if they share them, and so forth as this give a better indication of how popular the content is.

• Clean your dataset

You also want to clean your TikTok dataset before starting to have it scraped to remove any information that may affect the results you get. According to guttulus.com some of the things to clean out include null values, links, emojis, and so forth.

• Know where to get your data

You can decide to get your data from third-party sources or you could decide to do so directly from TikTok. All of these options have got their merits and demerits as is revealed in discussions on the same over at runrex.com, and it is up to you to decide which option suits you and your organization.

• Sentiment analysis

Sentiment analysis is also very important when researching TikTok posts and comments using data science. This will help you know how certain content is being received by users on the platform. You can get a positive signal, negative signal, or a neutral signal from content on TikTok which will let you know if the content hits the mark or not.

As always, if you are looking for more information on this and other related topics, then the excellent runrex.com and guttulus.com have got you covered and you should not hesitate to check them out.