  In sentiment classification, the enormous amount of textual data, its immense
dimensionality, and inherent noise make it extremely difficult for machine
learning classifiers to extract high-level and complex abstractions. In order
to make the data less sparse and more statistically significant, the
dimensionality reduction techniques are needed. But in the existing
dimensionality reduction techniques, the number of components needs to be set
manually which results in loss of the most prominent features, thus reducing
the performance of the classifiers. Our prior work, i.e., Term Presence Count
(TPC) and Term Presence Ratio (TPR) have proven to be effective techniques as
they reject the less separable features. However, the most prominent and
separable features might still get removed from the initial feature set despite
having higher distributions among positive and negative tagged documents. To
overcome this problem, we have proposed a new framework that consists of
two-dimensionality reduction techniques i.e., Sentiment Term Presence Count
(SentiTPC) and Sentiment Term Presence Ratio (SentiTPR). These techniques
reject the features by considering term presence difference for SentiTPC and
ratio of the distribution distinction for SentiTPR. Additionally, these methods
also analyze the total distribution information. Extensive experimental results
exhibit that the proposed framework reduces the feature dimension by a large
scale, and thus significantly improve the classification performance.
