  Twitter is among the most prevalent social media platform being used by
millions of people all over the world. It is used to express ideas and opinions
about political, social, business, sports, health, religion, and various other
categories. The study reported here aims to detect the tweet category from its
text. It becomes quite challenging when text consists of 140 characters only,
with full of noise. The tweet is categorized under 12 specified categories
using Text Mining or Natural Language Processing (NLP), and Machine Learning
(ML) techniques. It is observed that a huge number of trending topics are
provided by Twitter but it is really challenging to find out that what these
trending topics are all about. Therefore, it is extremely crucial to
automatically categorize the tweets into general categories for plenty of
information extraction tasks. A large dataset is constructed by combining two
different nature of datasets having varying levels of category identification
complexities. It is annotated by experts under proper guidelines for increased
quality and high agreement values. It makes the proposed model quite robust.
Various types of ML algorithms were used to train and evaluate the proposed
model. These models have explored over three datasets separately. It is
explored that the nature of the dataset is highly non-linear therefore complex
or non-linear models perform better. The best ensemble model named, Gradient
Boosting achieved an AUC score of 85\%. That is much better than the other
related studies conducted.
