15 Tips for Using Machine Learning to Research LinkedIn Posts & Comments
15 Tips for Using Machine Learning to Research LinkedIn Posts & Comments
As is revealed in discussions on the same over at runrex.com, LinkedIn is the world’s largest professional networking site. For brands and businesses on the platform, the massive amounts of data to be found there can be analyzed to extract valuable insights that can help them find the best candidates for positions among other benefits. Machine learning provides an excellent avenue to analyze data from LinkedIn and this article will look to list 15 tips for using ML to research LinkedIn posts and comments.
- Outline your objectives
As pointed out by the subject matter experts over at guttulus.com, before you get started with anything you should first outline the objectives of your project. By coming with the objectives for your project, you will be able to know the results you are aiming for as well as how to go about building your model which is why this is important.
- Collect your data
You will also need to collect your data once you have outlined your objectives for using machine learning to research LinkedIn posts and comments. According to runrex.com, it is important to collect data from sources that will give you the data you are looking to achieve your objectives given that it is with this data that you will train your models.
- Options for data collection
When looking to collect data for your project, it is also important that you know the options available to you as far as the collection of LinkedIn data is concerned. As outlined over at guttulus.com, these options include the official TikTok API and other unofficial APIs, publicly-available data sets, as well as web scraping tools and it is up to you to decide which avenue most fits your project.
- Clean your data
Once you have collected your data, it is important to note that LinkedIn posts and comments are examples of unstructured data which, as is discussed over at runrex.com, come with lots of ‘noise’. This is why cleaning your dataset to remove things like emojis, special characters, weblinks, and so forth, is an important tip worth remembering otherwise you will end up with inaccurate results.
- Clustering
Clustering is an unsupervised machine learning technique that involves taking a collection of items and partitioning them into smaller collections known as clusters according to some heuristic that is usually designed to compare the items in the collection. This is a technique that you should use when using ML to research LinkedIn posts and comments as it will help you understand that data better.
- Data normalization
When you extract LinkedIn data for research using machine learning, more often than not the data will not be provided in a format that you would have liked, and as such, as explained over at guttulus.com, a little munging is required to get it into a form that is suitable for analysis and this is where normalization comes in as it will help you achieve uniformity on the various aspects you are trying to analyze for the results you are looking.
- Computation of similarity
Once you achieve normalization, another thing that you may need to do is measure the similarity between any two of your data items. This can be job titles, company names, and any other field that can be entered as a variable-free text and this is where similarity computation comes in, as covered over at runrex.com, and is another step you shouldn’t skip when using machine learning to research LinkedIn posts and comments.
- Similarity computation may not always be obvious
According to discussions over at guttulus.com, there are some situations where similarity computing can be quite obvious, like say, when comparing the years of experience of two people. However, there are other situations where this may not be as obvious, like say, when comparing the leadership qualities of two people. This is something to also consider when using machine learning to research LinkedIn posts and comments.
- Natural language processing
Another tip worth highlighting when it comes to the use of machine learning to research LinkedIn posts and comments is how important it is not to forget about natural language processing. As is explained over at runrex.com, this technique will help you uncover valuable insights from your dataset such as the age, gender, location, and personal preferences of the LinkedIn user who wrote the post or comment you are analyzing.
- Machine learning platform
One of the things that people tend to play down is just how important it is to choose the right platform for building your machine learning model. It is, therefore, crucial that you choose a platform that is the right fit for you in terms of your project and finances, with MATLAB and IBM Watson Studio being examples of platforms to consider.
- Dimensionality reduction
If you are working with a huge set of LinkedIn data, then, according to guttulus.com, you will need to consider dimensionality reduction. This technique will allow you to organize items that are closely related, into a fixed number of bins allowing to comprehensively and exhaustively compare items within each bin to one another.
- Know the different types of sentiment analysis you can run
If you are hoping to use your machine learning model to run sentiment analysis, then it is important that you know that there are three types of sentiment analysis you can run. Fine-grained sentiment analysis is what most systems are based on and is the most common type, aspect-based sentiment analysis focuses on specific elements of the query, while intent sentiment analysis digs deeper, detecting people’s motivation behind their message; all of which are covered in detail over at runrex.com. Depending on your objectives, choose the one that is a correct fit for you.
- Know the limitations of sentiment analysis
Even as you run sentiment analysis on your LinkedIn dataset, you must know the limitations of this technique so that you can know the areas where your models may be lacking. As explained over at guttulus.com, some of the limitations of sentiment analysis include an inability to recognize irony and sarcasm, the fact that accuracy is tied to the quantity of data among other factors, and a challenge in interpreting context.
- Train and test your models
According to the subject matter experts over at runrex.com, the more you train and test your machine learning models with training data, the more accurate they will be in making predictions, which is why training and testing of your models is something that you should take very seriously.
- Refresh your data
It is also important that your models don’t go stale, which will happen if you are using stale data that is not relevant anymore. Regularly refresh your data and make sure that you are alive to the fact that users’ habits on LinkedIn change over time. If your models go stale, you will be unable to capture changes in user behavior or understand new trends.
This article only just begins to scratch the surface as far as this topic is concerned and you can uncover more insights on the topic by checking out runrex.com and guttulus.com.
