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Social Media Mining In The Fashion Mining

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Social Media Mining In The Fashion Mining


The concept of social media marketing (S.M.M.) has gradually been introduced in large and small scale businesses within the last ten years. Managers, company heads or C.E.O.s have noticed that use of social media in the industry plays a huge role in influencing consumer behaviour. Similarly, further research shows the impact of social media marketing in improving brand awareness, customer loyalty and commitment. Today, the uniqueness of social media data calls for the need to adopt data mining methods which can effectively analyze and extract user-generated content. The massive collection of data, gathered from social media comes, can potentially unfold exciting facts from the marketing sector. This social media data mostly appears in written contents like review comments, social groups and conversations with other groups of users.

This paper will, therefore, look for methods on how social media mining is being used in the fashion industry. It will answer crucial questions such as, how have these techniques been useful to achieving overall business success?

A Brief Case Study of The Josefinas Fashion Industry

The luxury brand called ‘Josefinas’ is famous for its wide variety of women fashion designs. The company uses social networks to spread advertising, build awareness and strengthen its business-customer relationship. Interestingly, the brand was launched back in 2013 to target a specific niche in the shoe industry. As it first started as a fully digital enterprise, the company is different from other large fashion brands. It’s a clear example of small scale enterprises which sell their products to a large number of the global population.

It’s, therefore, necessary for this social media mining study as the knowledge collected will significantly contribute to understanding the decisions associated with this type of data mining. Furthermore, the main objective is to create a quantitative model that can measure the impact of social network based on information gathered from social media websites such as Pinterest, Instagram and Facebook.

Improving Support Vector Machines (SVM) and Use of Sensitive Data Analysis

The knowledge and information extracted through social media mining are today being used in predicting changes in the fashion market for effective decision making. Support Vector Machines, for instance, are now being controlled by black-box social media data mining techniques. These are popular tools not only for classification but also for regression analysis. Using a sensitivity analysis method, they can assess how sensitive a new design or model is by, for example, finding the rate of output variation. Fashion industries are, however, finding it difficult to carry out such processes because of the constant changes in the input features. In many cases, such companies have to change such features through possible ranges of combinations, something that is computationally demanding.

To address these problems, companies need to adopt a data-based sensitivity analysis model. This often starts by complete extraction of a randomly chosen subset from the original set of data. Hence, such a process is less demanding as compared to changing all features through a variety of possible combinations, as it addresses how input features influence each other. This data-based sensitivity analysis method has been overly successful in different market studies and can be effectively implemented in the fashion sector.



Effective Social Network Interactions

Fashion marketers are increasingly incorporating social media mining in their brand strategy to not only to gather and analyze data but also to visualize and raise their brand awareness across social networks. They can also increase conversations for their products and brands while creating sufficient engagement. One good example is YouTube’s video mining which is currently demonstrating 100 million likes, comments, dislikes and shares from individual customers.

This means that fashion industries such as Josefinas can market their different sets of products on their YouTube channels and view customer feedback within a short time. They can also share product videos and photos between Facebook, Instagram, Facebook and Pinterest quickly. Platforms like Instagram one of the fastest-growing social networks in the fashion industry uses ‘visual storytelling’, to improve brand engagement due to its visual characteristics which complement the luxury standards in a majority of leading brands. Additionally, Twitter hashtags have provided clients with an agile approach to express their dislikes, likes and issue comments on new and already present fashion goods. These give fashion industries a ‘perfect’ chance to study and respond to customer comments or sentiments.


From this study, fashion industries need to invest more in gathering, analyzing and visualizing data. ‘Gathering’ will allow such industries to recognize social media discussions on topics or activities of interest. ‘Analyzing’ means expelling all the ‘noise’ and changes in the data, both structured and unstructured. By improving support vector machines and introducing sensitive data analysis techniques, fashion houses can boost their performance analysis, sentiment analysis, as well as trend analysis. Eventually, they can keep track of social network interactions through efficient visual analysis on the findings collected in sites such as Instagram, Twitter, Facebook and YouTube.



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