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machine learning in social media

Machine Learning Techniques for Social Media Intelligence

The use of artificial intelligence and machine learning systems will increasingly determine the effectiveness of the sharing platforms. How can companies exploit the large quantities of data produced and lay the foundations for new business models?

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Where Artificial Intelligence meets social networks

In the social media landscape, it is useful to have automated techniques to analyze user reactions on the products or services offered by a company.

By knowing the types of followers and users who leave comments on products can also be transformed into an advantage for the definition of future business plans. For example, this information can be useful to predict which products can be attractive for specific groups of users or to determine the best time to launch and present them.

Recognizing the potential of this aspect is important to examine opinion analysis tools and machine learning algorithms to improve analysis. The importance of machine learning for social network analysis is realized as a disruptive technology in these years. This is due to the unprecedented growth of social-related data, boosted by the proliferation of social media websites and the embedded heterogeneity and complexity.

Alongside the machine learning derives much effort from psychologists to build a computational model for solving tasks like recognition, prediction, planning, and analysis even in uncertain situations. Therefore, it is significant to study the synergy of machine learning techniques in social network analysis, focus on practical applications.

Social media intelligence

The more you know about a particular audience, the better your campaigns will perform. That’s how Social media listening works.

On the net, there are reportedly 95 million Instagram posts a day, 31.25 million Facebook posts per minute, and 6,000 tweets a second. (credits: Hootsuite) Artificial intelligence and machine learning help us to process large amounts of information.

Supervised learning and Text classifier

The best known and most adopted machine learning methods are supervised learning and unsupervised learning.

The supervised learning provides the machine’s computer system with a series of specific and coded notions, like models and examples that allow you to build a real database of information and experiences. In this case, the classification logic is given to the machine as input.

This permits the creation of a text classifier based on a neural network approach. A machine learning classifier works on a more abstract semantic level. With machine learning, it is possible to train the classifier by hand and make AI understand if a sentence has a positive or negative sentiment and its emotion.

Accuracy is very high in semantic textual analysis, as a classifier of this type reads the whole, not every single weighted word.

Image recognition

The social web has become visual-based,  with the huge success of Instagram, Snapchat or Pinterest. Posts on these platforms are mainly visual, and only a few hints are available in the content of the text.

In the past, identifying what’s in these posts was virtually impossible. Fortunately, that’s where deep learning comes to the rescue. These systems can now recognize logos, faces, and objects, in both images and videos. If you need to know when people are sharing your products on social media, image recognition is absolutely essential.

Topic and entities detection

Machine learning is useful to recognize patterns in the language, images or in the metadata. And we can now rely on these patterns to sort posts into predefined categories.

However, these patterns can also be used to detect new trends or topics that do not fit into a pre-existing set of values. The algorithm looks for interesting structures and tries to group similar examples.

These machine learning techniques are called “unsupervised,” and they highlight as a discovery tool or when new results fall outside what was expected.

Audience Profiling

Artificial Intelligence has important features to find audiences that are relevant to your company.

Information like demographics, interests and personality traits related to specific clusters are important to segment and to analyze the real people who generated conversations, and who talk about relevant topics every day. The profilation of the audiences created from social & web analytics is the last step of the Ai and machine learning journey inside social media intelligence.

Proper social media analysis requires the right tools. Social media are full of unimportant information. And there are too many conversations happening every day for you to possibly monitor them all manually. Artificial intelligence makes social media analysis more powerful, and more accurate.

At KPI6, we use machine learning algorithms in all our data enrichment steps to provide our customers with reliable and accurate insights on their brands, products, and ambassadors.

Would you like to know how a Digital Consumer Intelligence approach can help your Business?

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