4 Social Media Data Mining Techniques to Help Grow Your Online Business

Social media data mining has become a must-have strategy for understanding current trends, culture, and online business. This is because the world of social media is a thriving, ever-growing ocean of data, where hundreds of millions of tweets, instagram posts, and blog articles are published every day.

Data mining is a tool for making sense of social media. It’s a way of tracking what people are talking about online, how they’re talking about it, and where. To look at it another way, data mining is a method for uncovering trends, categorizing feedback, and making data-backed predictions based on the text, audio, images, and video that people use to express themselves online. 

Here’s a few example use cases:

• In ecommerce, data mining is used to analyze how people talk about products. 
• Bloggers and social media influencers use data analytics to help examine what their followers are talking about and how they feel about it.
• Brands use data mining to survey locations and make decisions regarding potential future markets.

In this article, I’ll look at 4 popular data mining techniques and provide links to deep-dive reads for each. I’ll also touch on getting started with your own project. By the end of this article you’ll hopefully have a general idea of some basic data mining techniques, and starting points for deeper research.

Social Media Data Mining Techniques

Keyword extraction: 

This is the process of extracting keywords to summarize or categorize a text. Keyword extraction is popular in data mining because it can reveal behavior and/or popular terms related to services or products.

The process can be as basic as scanning texts to create a list of the most-used words, or it can be tailored to search for and identify specific words and phrases. 

Keyword extraction can be used to find out what words people use to describe your products, or how they're talking about your latest video. By discovering words that are popular or unique to your audience, you can tailor future content to better connect with them.

Keyword extraction can also be used to categorize feedback, allowing for customer service teams to quickly identify issues or complaints based on keywords.

Further reading: Here’s a comprehensive look at text analysis tools, including keyword extraction, used for social media analysis.

Sentiment Analysis: 

Sentiment analysis is the process of analyzing opinion. This could include opinions regarding a new product line, reactions to a sporting event, or the current popularity of a politician or celebrity.

Though the type of opinion can be tailored to specific needs, at a basic level, sentiment analysis extracts words or phrases from a text (tweets, for example) to determine whether the text is positive, negative, or neutral.

Sentiment analysis is helpful for social media monitoring and analyzing the popularity of your brand. It can also be helpful for customer service, as you can uncover negative feedback, categorize them by urgency, and respond to them as necessary.

Further reading: This guide to sentiment analysis covers how it works, where it’s used, and how you can start trying it out yourself.

Market / Trend analysis:

Market trend analysis is the process of analyzing who your audience is. This means digging into what they’re passionate about, what’s trending in their community, and where they are. This is vital for connecting with your audience, because it tells you not just how people are talking about you or your brand, but also why, where, and when. 

Market analysis involves tracking keywords relevant to your brand or product, following trends, and analyzing where people are talking about you. This same analysis can be applied to understanding your competition, too.

The end result of this analysis is data that informs future decisions. For restaurants it can help with discovering popular menu items in particular regions.

For fashion brands it can help uncover new locations for focusing sales efforts. Social media influencers also use this kind of analysis to inform decisions about who to create content for.

Further reading: Sproutsocial’s guide to the importance of social media listening is easy to follow and filled with interesting ideas for a host of different industries.

Predictive Analytics:

Predictive analytics is the process of using past data to predict future trends. At its most basic, this means using historical data to build a model that captures important patterns. The model can then use new data to predict future developments.

In the fashion industry, predictive analytics has proven useful for revealing when trends are likely to go mainstream, and when they are on the decline. These forecasts are a result of analysis into social media activity along with search queries, ecommerce sell-throughs, and consumer feedback.

It’s worth mentioning here that unlike the other techniques mentioned above, predictive analytics is best when supplemented with data from other areas useful to your brand or business. It also requires a wealth of past data from which to train on before it can be especially useful making predictions.

Further reading: This article on predictive analytics covers why it matters, how it works, and also contains a few interesting use cases.

Starting a social media data mining project

If you’re looking to start your own project, your first step is data collection. There are a variety of ways to do this: you can access the social media API, scrape the web, or enlist the help of a data collection service.

Further reading: this guide to data collection tools covers options for a variety of project types. It’s worth checking out if you know your data needs and are researching how to get it.

There are pros, cons, and unique difficulties for each data collection method, but first and most importantly: make sure you are legally allowed to access the data.

What to do next depends on your needs. A small business could be fine with just the services of a data mining API for access to analytics visualizations. It’s also possible to build your own data analytics platform if you have programming experience; this is an option to consider if what you want to analyze is very specific or otherwise unavailable.

Bigger brands, however, are more likely to invest in a data-driven platform with deeper levels of analysis customized for their needs.

Whatever the case, if you’re starting on a data mining expedition, start by locating the social media platform your audience calls home.

From there, define your data analysis needs and how the results will be put to use. Knowing this will help you understand what particular data you need and how much data is necessary. 

Data mining and data collection are of course more complex than this introductory article, so be sure to dive in and do your research before jumping into your own project!

I hope this article has been a good introduction to social media data mining techniques and what they do. If you have any comments or questions, feel free to hit me up on Twitter.