Support Vector Machines (SVMs)
Overview
Support Vector Machines (SVMs) are a popular machine learning algorithm used for both classification and regression tasks. SVMs work by finding the hyperplane in a high-dimensional space that maximally separates the data into different classes or groups. The hyperplane is chosen to have the largest margin or distance to the nearest data points of each class. SVMs are effective in handling datasets with a high number of features and are known for their ability to handle non-linearly separable data using the kernel trick.
For the text mining topic "Is Nike the top-ranked brand in the global footwear market?: An Analysis of brand recognition and customer appeal compared to other leading shoe brands," SVMs can be used to classify shoe brands based on different input variables such as customer reviews, social media mentions, and sales data. The SVM algorithm can be trained on labeled data to identify the features or input variables that best separate the different shoe brands and create a decision boundary to distinguish them. SVMs can also be used to identify the most influential features or attributes that contribute to a shoe brand's success and customer appeal. By using SVMs on this topic, researchers can gain insights into the key factors that drive brand recognition and customer appeal and compare the performance of different shoe brands in the global market.
Data Preparation
Like all supervised machine learning algorithms, SVMs require labeled data to be effective. In the context of the text mining topic "Is Nike the top-ranked brand in the global footwear market?: An Analysis of brand recognition and customer appeal compared to other leading shoe brands," labeled data could be customer reviews, social media mentions, and sales data that are already categorized or labeled based on the shoe brand they are discussing. This labeled data is used to train the SVM model to identify the features or input variables that best separate different shoe brands and create a decision boundary to distinguish them. Without labeled data, the SVM model cannot be trained, and its predictions will not be accurate. Therefore, it is crucial to have labeled data for supervised modeling, including SVMs, to be effective in making accurate predictions and gaining insights into the key factors that drive brand recognition and customer appeal in the global footwear market.
Code
Results
Visualization
Positive and Negative coefficient
3 different kernels with accuracy and Confusion Matrix
Conclusion
In conclusion, SVMs can be a useful tool in analyzing the text mining topic "Is Nike the top-ranked brand in the global footwear market?: An Analysis of brand recognition and customer appeal compared to other leading shoe brands." By using labeled data to train the SVM model, researchers can identify the most influential features and attributes that contribute to a shoe brand's success and customer appeal. This information can be used to compare the performance of different shoe brands in the global market, identify the factors that differentiate the top-ranked brand from others, and predict the success of new shoe brands. Overall, SVMs can provide valuable insights into the global footwear market and help inform strategic decisions for both established and new shoe brands looking to improve their brand recognition and customer appeal.