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 …

Sentiment analysis, a specialization of natural language processing (NLP), has witnessed
significant progress since its emergence in the late 1990s, owing to the swift advances in …

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 …

Linguistic typology aims to capture structural and semantic variation across the world's
languages. A large-scale typology could provide excellent guidance for multilingual Natural …

Many intelligent systems in business, government or academy process natural language as
an input during inference or they might even communicate with users in natural language …

In many languages, sparse availability of resources causes numerous challenges for textual
analysis tasks. Text classification is one of such standard tasks that is hindered due to …

Cross-lingual sentiment classification (CLSC) aims to leverage rich-labeled resources in the
source language to improve prediction models of a resource-scarce domain in the target …

Obtaining high-quality labelled data for training a classifier in a new application domain is
often costly. Transfer Learning (aka “Inductive Transfer”) tries to alleviate these costs by …

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 …

Multilingual sentence encoders have seen much success in cross-lingual model transfer for
downstream NLP tasks. The success of this transfer is, however, dependent on the model's …