Semi-supervised vs. Cross-domain Graphs for Sentiment Analysis

Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification

We consider the cross-domain sentiment classification problem, where a sentiment classifier is to be learned from a source domain and to be generalized to a target domain. Our approach explicitly minimizes the distance b…

A Semi-supervised Approach for a Better Translation of Sentiment in Dialectical Arabic UGT

In the online world, Machine Translation (MT) systems are extensively used to translate User-Generated Text (UGT) such as reviews, tweets, and social media posts, where the main message is often the author's positive or …

Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy

Label scarcity in a graph is frequently encountered in real-world applications due to the high cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a la…

Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora

There is often the need to perform sentiment classification in a particular domain where no labeled document is available. Although we could make use of a general-purpose off-the-shelf sentiment classifier or a pre-built…

ReNew: A Semi-Supervised Framework for Generating Domain-Specific Lexicons and Sentiment Analysis