Social Media Data Mining Techniques to Expand Online Business
3 Social Media Data Mining Techniques that can help grow Business Online
Data mining is a process used by companies to convert raw data into useful information. Businesses can learn more about their customers to develop more effective marketing strategies, increase sales, and reduce costs by using software to look for patterns in batches of data. Data mining depends on rigorous data collection, warehousing, and computer processing.
It involves exploring and analyzing vast blocks of information to glean meaningful patterns and trends. It can be used in numerous ways, such as database marketing, credit risk management, fraud detection, spam email filtering, or even to discern users’ sentiment or opinion. Data Mining is now being used for social media as well.
Social media data mining is done on a much larger scale than traditional data mining. While data mining happens within a company’s internal databases and systems, social media data mining is far less limited than what and where it explores. After social data is mined, outcomes are passed on to social media analytics software to explain and visualize the insights.
Social data first requires gathering and processing. This data is publicly available, including age, sex, race, geographic location, job profession, and more. Then there’s the unstructured content that one posts on social media, such as tweets, status updates, comments. If one’s social media profiles are public, understand this is a fair game for social media mining. In short, social media data mining is a way of tracking people’s online activities.
Following are a few use cases of social media data mining:
- Bloggers and social media influencers use data analytics to examine what their followers are talking about and their feelings about it.
- In eCommerce, data mining is used to analyze what people talk about products available online.
- Brands use data mining to survey locations and make decisions regarding potential future markets.
A variety of data mining techniques are applied to keep track of one user’s social media handles. Some techniques might utilize machine learning. It entirely depends on how deep the ‘miners’ are looking to explore. Let’s look at social media data mining techniques.
Trend analysis can be a fundamental metric for businesses that use social listening. For instance, businesses may analyze which topics, mentions, and keywords on social media are currently trending and apply techniques to understand the reason.
Market or trend analysis involves tracking keywords relevant to a brand or product, following trends, and analyzing where people are talking about the individual. This analysis can be used to understand competition, as well. As a result of this analysis, data informs about future decisions. For restaurants or cafes, it can help with discovering popular menu items in particular regions.
Sentiment analysis is the process of analyzing opinion means regarding a new product line, reactions to a sporting event, or the current popularity of a politician or celebrity.
For instance, SimplyMeasured’s recent analysis shows that mining sentiment on social media platforms, such as Twitter and Facebook leading up to the 2016 U.S Presidential Election was more accurate at speculating the election’s results. Although many regular polls that year projected Hillary Clinton to be the winner, the-candidate Donald Trump had a more positive sentiment on social media than his opponent.
Extracting keywords is another technique of social media mining to summarize or categorize a text. This technique is quite popular in data mining because it can reveal behavior or popular terms linked with services and products. Keyword extraction can be as necessary as scanning texts to make a list of the most-used words. It can be tailored to search for and detect specific words and phrases.
This technique can quickly determine what words people use to describe a particular product or talk about a viral video. One can tailor future content to connect with people better by discovering famous or unique words to the audience.
Keyword extraction can also be applied to categorize feedback, allowing customer service teams to recognize issues or complaints based on keywords quickly.
With technological advancements like artificial neural networks and machine learning, social media data mining will continue to get more creative and widely used by businesses. Meanwhile, ensure visualizing results in a way a broader audience can understand. Social media analytics frequently provide their own visualization. However, it may be worth seeing which data visualization options are available out there for more advanced users.