Abstract In Machine Learning (ML), Feature Selection (FS) plays a crucial part in reducing
data's dimensionality and enhancing any proposed framework's performance. However, in …

Feature Selection (FS) methods alleviate key problems in classification procedures as they
are used to improve classification accuracy, reduce data dimensionality, and remove …

Internet and social media platforms such as Twitter, Facebook, and several blogs provide
various types of helpful information worldwide. The increased usage of social media and e …

A tremendous amount of unstructured data, such as comments, opinions, and other sorts of
data is generated in real-time with the growth of web 2.0. Due to the unstructured nature of …

The advent of different social networking sites has enabled anyone to easily create, express,
and share their ideas, thoughts, opinions, and feelings about anything with millions of other …

In recent years, sentiment analysis is becoming more and more important as the number of
digital text resources increases in parallel with the development of information technology …

Cognitive computing is an interdisciplinary research field that simulates human thought
processes in a computerized model. One application for cognitive computing is sentiment …

Numerous studies on mental depression have found that tweets posted by users with major
depressive disorder could be utilized for depression detection. The potential of sentiment …

Online reviews are becoming increasingly important for decision-making. Consumers often
refer to online reviews for opinions before making a purchase. Marketers also acknowledge …

The feature selection (FS) has been the latest challenge in the area of sentiment
classification. The filter-and wrapper-based feature selection methods are applied in the …