Clustering textual content paperwork is a typical problem in pure language processing (NLP). Based mostly on their content material, associated paperwork are to be grouped. The k-means clustering approach is a popular answer to this problem. On this article, we’ll exhibit easy methods to cluster textual content paperwork utilizing k-means utilizing Scikit Be taught.
Okay-means clustering algorithm
The k-means algorithm is a popular unsupervised studying algorithm that organizes information factors into teams primarily based on similarities. The algorithm operates by iteratively assigning every information level to its nearest cluster centroid after which recalculating the centroids primarily based on the newly shaped clusters.
Preprocessing describes the procedures used to get information prepared for machine studying or evaluation. It ceaselessly entails remodeling, reformatting, and cleansing uncooked information and vectorization right into a format applicable for extra evaluation or modeling.
- Loading or making ready the dataset [dataset link: https://github.com/PawanKrGunjan/Natural-Language-Processing/blob/main/Sarcasm%20Detection/sarcasm.json]
- Preprocessing of textual content in case the textual content is loaded as a substitute of manually including it to the code
- Vectorizing the textual content utilizing TfidfVectorizer
- Cut back the dimension utilizing PCA
- Clustering the paperwork
- Plot the cluster utilizing matplotlib
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