Social media popularity and importance is on the increase due to people using it for various
types of social interaction across multiple channels. This systematic review focuses on the …

Sentiment analysis is essential in many real-world applications such as stance detection,
review analysis, recommendation system, and so on. Sentiment analysis becomes more …

The growth of the Internet has expanded the amount of data expressed by users across
multiple platforms. The availability of these different worldviews and individuals' emotions …

Language models are ubiquitous in current NLP, and their multilingual capacity has recently
attracted considerable attention. However, current analyses have almost exclusively focused …

Sentiment Analysis (SA) is an application of Natural Language Processing (NLP) to extract
the sentiments expressed in the text. In this paper, we experimented five approaches to …

In this paper, we propose a novel hybrid deep learning archtecture which is highly efficient
for sentiment analysis in resource-poor languages. We learn sentiment embedded vectors …

Sentiment Analysis is currently considered as one of the most attractive research topics in
Natural Language Processing (NLP) field. The main objective of sentiment analysis is to …

Sentiment analysis on social media relies on comprehending the natural language and
using a robust machine learning technique that learns multiple layers of representations or …

Sentiment analysis is one of the most fundamental tasks in Natural Language Processing.
Popular languages like English, Arabic, Russian, Mandarin, and also Indian languages such …

To obtain extensive annotated data for under-resourced languages is challenging, so in this
research, we have investigated whether it is beneficial to train models using multi-task …