In text mining, feature selection (FS) is a common method for reducing the huge number of
the space features and improving the accuracy of classification. In this paper, we propose an …

Abstract Online Social Networks (OSNs) are the most popular interactive media for
communicating, posting, and sharing indefinite amounts of personal information. However …

Binary sentiment analysis uses sentiment dictionaries, TF-IDF, word2vec, and BERT to
convert text documents such as product and movie reviews into vectors. Dimensionality …

Text mining is one of the most significant field in the digital era due to the rapid growth of
textual information. Topic models are gaining popularity in the last few years. A topic …

In recent decades, social media sites such as twitter, facebook, and review site produces
huge number of textual information posted by many users. The user tends to express his/her …

In recent internet era, micro-blogging sites produce enormous amount of short textual
information, which appears in the form of opinions or sentiments of users. Sentiment …

Textual Entailment Recognition (TER), also known as natural language inference, is a
crucial task in natural language processing that combines many fundamental aspects of …

This paper analyzes users' reactions on Twitter to the COVID-19 pandemic, using machine
learning and data mining algorithms to classify tweets according to economic and health …

With the advent of micro-blogging sites, users are pioneer in expressing their sentiments
and emotions on global issues through text. Automatic detection and classification of …

Wrapper Feature Selection (FS) methods based on the Binary Bat Algorithm (BBA) have
recently been employed in a variety of detection applications to determine the most relevant …