Sentiment analysis has come long way since it was introduced as a natural language
processing task nearly 20 years ago. Sentiment analysis aims to extract the underlying …

Twenty-four studies on twenty-three distinct languages and eleven social media illustrate the
steady interest in deep learning approaches for multilingual sentiment analysis of social …

The inception and rapid growth of the Web, social media, and other online forums have
resulted in the continuous and rapid generation of opinionated textual data. Several real …

Abstract Analysis of subjective texts like offensive content or hate speech is a great
challenge, especially regarding annotation process. Most of current annotation procedures …

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 …

Analyzing the sentiments of people from social media content through text, speech, and
images is becoming vital in a variety of applications. Many existing research studies on …

Abstract Analysis of emotions elicited by opinions, comments, or articles commonly exploits
annotated corpora, in which the labels assigned to documents average the views of all …

We propose and test multiple neuro-symbolic methods for sentiment analysis. They combine
deep neural networks–transformers and recurrent neural networks–with external knowledge …

Traditional methods for sentiment analysis, when applied in a monolingual context, often
yield less than optimal results in multilingual settings. This underscores the need for a more …

For subjective NLP problems, such as classification of hate speech, aggression, or
emotions, personalized solutions can be exploited. Then, the learned models infer about the …