  Text classification is a fundamental problem in the field of natural language
processing. Text classification mainly focuses on giving more importance to all
the relevant features that help classify the textual data. Apart from these,
the text can have redundant or highly correlated features. These features
increase the complexity of the classification algorithm. Thus, many
dimensionality reduction methods were proposed with the traditional machine
learning classifiers. The use of dimensionality reduction methods with machine
learning classifiers has achieved good results. In this paper, we propose a
hybrid feature selection method for obtaining relevant features by combining
various filter-based feature selection methods and fastText classifier. We then
present three ways of implementing a feature selection and neural network
pipeline. We observed a reduction in training time when feature selection
methods are used along with neural networks. We also observed a slight increase
in accuracy on some datasets.
