This study describes a hybrid semantic knowledgebase-machine learning strategy for
mining domain feature-level opinions and categorizing them on a multi-point scale. A …

This study aimed to analyse public sentiments of UK-originated tweets related to COVID-19
vaccines, and it applied six chronological time periods, between January and December …

Abstract Domain adaptation in sentiment analysis is one of the areas where a classifier
trained in one domain often classifies sentiments poorly when applied to another domain …

Neural networks, primarily recurrent and convolutional Neural networks, have been proven
successful in text classification. However, convolutional models could be limited when …

“Social sensors” refer to those who provide opinions through electronic communication
channels such as social networks. There are two major issues in current models of …

The purpose of cross‐domain opinion classification is to leverage useful information
acquired from the source domain to train a classifier for opinion classification in the target …

Despite the importance of emphasizing the right psychotherapy treatment for an individual
patient, assessing the outcome of the therapy session is equally crucial. Evidence showed …

Automatic opinion mining of social media textual data is crucial for determining people's
attitudes and improving decision making. Since most of the reviews are unstructured, they …

We performed an analysis of tweets concerning the COVID-19 pandemic in the UK over a
two-year period, selecting fifteen timelines. Over 110,000 tweets were obtained from Twitter …

This study addresses the issue of recognising customer intent when only limited training
data is available. The performance of ChatGPT was evaluated in this scenario, and it was …