Text embedding techniques for efficient clustering of twitter data.

World wide web is abundant with various types of information such blogs, social media posts, news articles. With this type of magnitude of online content, there is a need to deeply understand the insights of it in order to make use of the information for practical applications such as event detection, polarity, sentiment analysis and so on. Natural Language Processing (NLP) is the study of such information which is used for text classification, sentiment analysis, clustering of similar text. NLP makes use of linguistic knowledge and build machine learning models to analyse textual information. NLP finds its way in various applications like classification of online review into positive and negative without actually reading the reviews and feedback. For text analysis, there should be a way to quantify the text based on its frequency of occurrence, correlation with neighbouring words, contextual similarity of words, etc. One such way is word embedding. This study applies various word embedding techniques on tweets of popular news channels and clusters the resultant vectors using K-means algorithm. From this study, it is found out that Bidirectional Encoder Representations from Transformers (BERT) has achieved highest accuracy rate when used with K-means clustering.