Sentiment analysis (SA), also called Opinion Mining (OM) is the task of extracting and
analyzing people's opinions, sentiments, attitudes, perceptions, etc., toward different entities …

Cross-lingual sentiment analysis (CLSA) leverages one or several source languages to help
the low-resource languages to perform sentiment analysis. Therefore, the problem of lack of …

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 …

The objective of this article is to present a hybrid approach to the Sentiment Analysis
problem at the sentence level. This new method uses natural language processing (NLP) …

The new transformed read-write Web has resulted in a rapid growth of user generated
content on the Web resulting into a huge volume of unstructured data. A substantial part of …

Deep neural network models have achieved remarkable results in sentiment classification.
Traditional feature-based methods perform slightly worse than deep learning methods in …

Contextual word embeddings (eg GPT, BERT, ELMo, etc.) have demonstrated state-of-the-
art performance on various NLP tasks. Recent work with the multilingual version of BERT …

Recent advances in large pretrained language models have increased attention to zero-shot
text classification. In particular, models finetuned on natural language inference datasets …

This paper investigates the ability of multilingual BERT (mBERT) language model to transfer
syntactic knowledge cross-lingually, verifying if and to which extent syntactic dependency …

Deep neural networks (DNN) have achieved remarkable results in sentiment classification.
Some ensemble methods of DNN models and traditional feature-based models are …