15 Tips for Using Machine Learning to Research TikTok Posts & Comments
TikTok is the new sensation within the social media circles having been among the most downloaded apps in the App Store and Google Play Store this year as covered over at runrex.com. With the huge amounts of data to be found over there and the growing influence of machine learning in analyzing and extracting useful insights from social media platforms, it is no surprise that the use of ML on the platform has been on the rise. This article will look to help those looking to get into it by highlighting 15 tips for using machine learning to research TikTok posts and comments.
- Come up with your objectives
Before you even go any further, if you are planning to use machine learning to research TikTok posts and comments, then setting out the objectives for your project should be the first thing you do according to the gurus over at guttulus.com. It is always good to have a plan in place that outlines what you are looking to achieve and this will help you do that.
- Know the techniques available to you
Machine learning is a broad term that consists of many techniques as covered in detail over at runrex.com. Once you have outlined your objectives, therefore, you should then decide which of these techniques will help you achieve the same. Here, you can opt for Topic Modelling, sentiment analysis, social media monitoring, among other techniques.
- Collect your data
Once you have decided what machine learning technique you will be employing, the next step is to collect your data. As is explained over at guttulus.com, you must collect data that will give you the results you are looking for based on your objectives as even if you build impressive models, it won’t make a difference if you got this part wrong.
- Options available to you as far as data gathering is concerned
It is also important that you know what options are at your disposal when it comes to collecting TikTok data to analyze with machine learning models. From discussions over at runrex.com, the options to consider here are the official TikTok API, third-party APIs, web scraping tools, and public data sets. Choose the right option for you.
- Leverage natural language processing
When using machine learning to research TikTok posts and comments, you must take full advantage of natural language processing (NLP). This will offer you valuable clues about the gender, location, preferences, and even the age of the author of the given post or comment.
- Choose the right platform to build your models
According to the subject matter experts over at guttulus.com, given that it will play an important role in determining the eventual accuracy and predictive power of your model, it is important to choose the right platform to build your machine learning models. There are several top options to consider including IBM Watson Studio, MATLAB, Anaconda Enterprise, among others.
- Clean your data
Posts and comments on TikTok fall under the category of unstructured data as explained over at runrex.com since they don’t follow any rules. This means that TikTok data usually contains a lot of ‘noise’ which is why before you feed it into your machine learning model you should have it cleaned to remove things like misspelled words, special characters, emojis, and other such things that will make your results inaccurate.
- Focus on aspect-based sentiment analysis
As is outlined over at guttulus.com, aspect-based sentiment analysis allows you to go beyond just categorizing sentiment with polarity, and allows you to focus on specific elements of the content. This will let you know about sentiments on specific aspects of your content or brand, which will be more helpful.
- Know your options as far as sentiment analysis tools are concerned
When using machine learning to research TikTok posts and comments, it is important to know some sentiment analysis tools you can lean on. From discussions on the same over at runrex.com, examples of some good sentiment analysis tools include Keyhole, Brandwatch, Sprout Social, among others, and you can choose which one is the right fit for you.
- Keep an eye on sentiment score
Sentiment is usually measured with a sentiment score which gives you the overall value of what you are tracking, be it your brand, a specific hashtag, and so forth. The sentiment score is calculated by considering both the positive and negative posts and is an important part of sentiment analysis hence why you should keep a close eye on it.
- Know the limitations of sentiment analysis
When using machine learning to research TikTok posts and comments, it is important to note that techniques such as sentiment analysis have got some limitations. As is covered over at guttulus.com, some of these limitations include that models find it hard to detect irony and sarcasm as well as analyzing the right context.
- Refresh your data
TikTok is popular for its fun and engaging trending topics and hashtags. However, one thing to note about these trends is that they change very quickly, and what may be trending now may be out of favor in a couple of days. This is why an important tip when using machine learning to research TikTok posts is to keep your data fresh to prevent your models from going stale as discussed over at runrex.com, which will affect results.
- Online learning models and periodic training
From discussions on the same over at guttulus.com, one way to keep your machine learning models fresh is to have online learning models or at least some form of periodic training. This will ensure that the data you are working on is regularly refreshed preventing your models from going stale.
- Exhaustively train and test your models
Once you build your machine learning models for TikTok, it is important that you are thorough when training and testing them. Train them with as many tags as possible, and make corrections where the models are wrong keeping in mind that, as pointed out by the gurus over at runrex.com, the more you train and test your model with training data, the more accurate it will be in predicting.
- Beware if multipolarity
Sometimes you will find someone expressing an opinion about something on TikTok that is both positive and negative, offering both praise and criticism within the same sentence. To ensure that you get accurate results when analyzing such sentences, make sure that your models assign a polarity to each aspect of the text.
Remember, if you are looking for more information on this and other related topics, then you should look no further than the highly-rated runrex.com and guttulus.com.