For many years, researchers in the field of natural language processing have been
exploring sentiment analysis, a method for understanding human feelings and thoughts …

Over the last decade, the use of social media as a massive communication medium has
given people a tool to express their opinions. In it, people write their thoughts and feelings …

Abstract Machine learning methods, especially deep learning models, have achieved
impressive performance in various natural language processing tasks including sentiment …

Contemporary machine learning models, like deep neural networks, require substantial
labeled datasets for proper training. However, in areas such as natural language …

In synonym replacement-based data augmentation techniques for natural language
processing tasks, words in a sentence are often sampled randomly with equal probability. In …

In deep learning-based models, such as those used for sentiment analysis, one critical
challenge is ensuring robust and generalized performance. While data augmentation has …

A significant part of natural language processing (NLP) techniques for sentiment analysis is
based on supervised methods, which are affected by the quality of data. Therefore …

Data augmentation is a commonly-used technique to avoid over-fitting in deep learning.
However, the mechanism behind effective data augmentation methods is unclear. To …

Sentiment analysis is an application of natural language processing that requires an
abundance of data that may not be achieved sometimes for some reason. Data …

Sentiment analysis (SA) is a popular method for collecting relevant and arbitrary information
from text-based data. To locate, examine, extract reactions, and emotions from the data or …