Sentiment analysis is a critical subfield of natural language processing that focuses on
categorizing text into three primary sentiments: positive, negative, and neutral. With the …

Transformer architectures are highly expressive because they use self-attention
mechanisms to encode long-range dependencies in the input sequences. In this paper, we …

We live in a digitized era where our daily life depends on using online resources.
Businesses consider the opinions of their customers, while people rely on the …

With the exponential growth of social media platforms and online communication, the
necessity of using automated sentiment analysis techniques has significantly increased …

Sentiment Analysis (SA) of text reviews is an emerging concern in Natural Language
Processing (NLP). It is a broadly active method for analyzing and extracting opinions from …

In the field of text summarization, extractive techniques aim to extract key sentences from a
document to form a summary. However, traditional methods are not sensitive enough to …

Digital platforms on the internet are invaluable for collecting user feedback, suggestions,
and opinions about various topics, such as company products and services. This data is …

Abstract Machine learning and deep learning models are increasingly susceptible to
adversarial attacks, particularly in critical areas like cybersecurity and Information Disorder …

Sentiment analysis towards short texts is always facing challenges, because short texts only
contain limited semantic characteristics. As a result, this paper constructs a specific large …

Transformers have made significant strides across various artificial intelligence domains,
including natural language processing, computer vision, and audio processing. This …