  Sentiment analysis is a domain of study that focuses on identifying and
classifying the ideas expressed in the form of text into positive, negative and
neutral polarities. Feature selection is a crucial process in machine learning.
In this paper, we aim to study the performance of different feature selection
techniques for sentiment analysis. Term Frequency Inverse Document Frequency
(TF-IDF) is used as the feature extraction technique for creating feature
vocabulary. Various Feature Selection (FS) techniques are experimented to
select the best set of features from feature vocabulary. The selected features
are trained using different machine learning classifiers Logistic Regression
(LR), Support Vector Machines (SVM), Decision Tree (DT) and Naive Bayes (NB).
Ensemble techniques Bagging and Random Subspace are applied on classifiers to
enhance the performance on sentiment analysis. We show that, when the best FS
techniques are trained using ensemble methods achieve remarkable results on
sentiment analysis. We also compare the performance of FS methods trained using
Bagging, Random Subspace with varied neural network architectures. We show that
FS techniques trained using ensemble classifiers outperform neural networks
requiring significantly less training time and parameters thereby eliminating
the need for extensive hyper-parameter tuning.
