Nowadays, with the increasing number of Web 2.0 tools, users generate huge amounts of
data in an enormous and dynamic way. In this regard, the sentiment analysis appeared to be …

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 …

Humans are increasingly integrated with devices that enable the collection of vast
unstructured opinionated data. Accurately analysing subjective information from this data is …

Sentiment analysis is a crucial Natural Language Processing task to analyze the user's
emotions and opinions towards events, entities, services, or products. Arabic NLP faces …

Sentiment analysis automatically evaluates people's opinions of products or services. It is an
emerging research area with promising advancements in high-resource languages such as …

This paper proposes a framework for training language-invariant cross-modal retrieval
models. We also introduce a novel character-based word-embedding approach, allowing …

Neural architecture search (NAS) has been demonstrated to be promising in deep learning
for text classification. Most existing NAS algorithms, however, are proposed for single …

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 …

With the explosive growth of social media, the online community can freely express their
opinions without disclosing their identities. People with hidden agendas can easily post fake …