For decades, researchers have experimented with the possibility that machines can equal
human linguistic capabilities. Recently, advances in the field of natural language processing …

In the last few decades, social media usage has exponentially increased, and people often
share information covering various topics of interest. The social media platforms such as …

In recent years, the use of social media platforms has surged significantly. Social media
users convey their views in the form of feedback about different commercial products …

Sentiment lexicon learning is of paramount importance in sentiment analysis. One of the
most considerable challenges in learning sentiment lexicons is their domain-specific …

Abstract Unlike crowdsourcing, Spatial Crowdsourcing (SC) requires workers to travel to a
specific physical location to accomplish a task. Due to its open concept, the platform accepts …

Text mining methods usually use statistical information to solve text and language-
independent procedures. Text mining methods such as polarity detection based on …

Within multi-domain sentiment analysis, we study how different domain-dependent polarities
can be learned for the same concepts. To this aim, we extend an existing approach based …

Due to the varying data distributions in different domains, transferring sentiment
classification models across domains is often infeasible. Additionally, labeling data in …

This study delves into the transformative impact of leveraging large language models (LLMs)
in marketing analytics, particularly emphasising a paradigm shift from fine-tuning models to …

In this paper we present an in-depth analysis of sentiment. It also offers a survey and
discusses the latest papers and techniques relevant to strategies for lexicon-based …