Multiclass Sentiment Classification of Online Health Forums using Both Domain-independent and Domain-specific Features

Alnashwan, Rana; Sorensen, Humphrey; O'Riordan, Adrian; Hoare, Cathal

doi:10.1145/3148055.3148058

Cited by 3 publications

(1 citation statement)

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“…In this article, they addressed the categories of emotions and classified user comments into the emotion groups found in online medical forums. Machine learning classifiers such as Random Forest, Logistic Regression and Neural Network used for an automated multi-class classification framework [29]. This article highlighted an aspect-level approach for sentiment analysis and examined the diabetes-related topics on the Twitter and classification approach dependent on SentiWordNet scores [30].…”

Section: Traditional Sentiment Analysis Methodsmentioning

confidence: 99%

Domain adaptive learning for multi realm sentiment classification on big data

Ijaz,

Anwar,

Safran

et al. 2024

PLoS ONE

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Machine learning techniques that rely on textual features or sentiment lexicons can lead to erroneous sentiment analysis. These techniques are especially vulnerable to domain-related difficulties, especially when dealing in Big data. In addition, labeling is time-consuming and supervised machine learning algorithms often lack labeled data. Transfer learning can help save time and obtain high performance with fewer datasets in this field. To cope this, we used a transfer learning-based Multi-Domain Sentiment Classification (MDSC) technique. We are able to identify the sentiment polarity of text in a target domain that is unlabeled by looking at reviews in a labelled source domain. This research aims to evaluate the impact of domain adaptation and measure the extent to which transfer learning enhances sentiment analysis outcomes. We employed transfer learning models BERT, RoBERTa, ELECTRA, and ULMFiT to improve the performance in sentiment analysis. We analyzed sentiment through various transformer models and compared the performance of LSTM and CNN. The experiments are carried on five publicly available sentiment analysis datasets, namely Hotel Reviews (HR), Movie Reviews (MR), Sentiment140 Tweets (ST), Citation Sentiment Corpus (CSC), and Bioinformatics Citation Corpus (BCC), to adapt multi-target domains. The performance of numerous models employing transfer learning from diverse datasets demonstrating how various factors influence the outputs.

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Section: Traditional Sentiment Analysis Methodsmentioning

confidence: 99%