Sentiment analysis can be used to derive knowledge that is connected to emotions and
opinions from textual data generated by people. As computer power has grown, and the …

Social media platforms provide a goldmine for mining public opinion on issues of wide
societal interest and impact. Opinion mining is a problem that can be operationalised by …

SenticNet is the knowledge base which the sentic computing framework leverages on for
concept-level sentiment analysis. This chapter illustrates how such a resource is built. In …

In this work, we propose BERT-WMAL, a hybrid model that brings together information
coming from data through the recent transformer deep learning model and those obtained …

In recent years, the explainable artificial intelligence (XAI) paradigm is gaining wide
research interest. The natural language processing (NLP) community is also approaching …

Making machines understand language and reasoning on it has been one of the most
challenging problems addressed by Artificial Intelligent researchers. This challenge …

Stereotypes about immigrants are a type of social bias increasingly present in the human
interaction in social networks and political speeches. This challenging task is being studied …

Hate speech detection mostly involves the use of text data. This data, usually sourced from
various social media platforms, have been known to be plagued with numerous issues that …

This paper argues in favor of the adoption of annotation practices for multimodal datasets
that recognize and represent the inherently perspectivized nature of multimodal …

The number of applications in sentiment analysis is growing daily, and research in this field
is increasing. Despite the rapid growth of data sources in English, low-resource languages …