  The exponential growth of data generated on the Internet in the current
information age is a driving force for the digital economy. Extraction of
information is the major value in an accumulated big data. Big data dependency
on statistical analysis and hand-engineered rules machine learning algorithms
are overwhelmed with vast complexities inherent in human languages. Natural
Language Processing (NLP) is equipping machines to understand these human
diverse and complicated languages. Text Classification is an NLP task which
automatically identifies patterns based on predefined or undefined labeled
sets. Common text classification application includes information retrieval,
modeling news topic, theme extraction, sentiment analysis, and spam detection.
In texts, some sequences of words depend on the previous or next word sequences
to make full meaning; this is a challenging dependency task that requires the
machine to be able to store some previous important information to impact
future meaning. Sequence models such as RNN, GRU, and LSTM is a breakthrough
for tasks with long-range dependencies. As such, we applied these models to
Binary and Multi-class classification. Results generated were excellent with
most of the models performing within the range of 80% and 94%. However, this
result is not exhaustive as we believe there is room for improvement if
machines are to compete with humans.
