When building machine learning model for text classification, there are a lot of features. Because the features are made from words, broader context of the corpus, higher dimensional for features. It happens when i build machine learning for news classification, sentiment analysis, web page classification, and so on.
Linear Support Vector Machine is widely regarded as one of the best text classification algorithms. We achieve a higher accuracy score of 79% which is 5% improvement over Naive Bayes.
Some examples of text classification are: Understanding audience sentiment from social media, Detection of spam and non-spam emails, Auto tagging of customer queries, and
