Dataset Information

Emotional Variance Analysis: A new sentiment analysis feature set for Artificial Intelligence and Machine Learning applications.

ABSTRACT: Sentiment Analysis (SA) is a category of data mining techniques that extract latent representations of affective states within textual corpuses. This has wide ranging applications from online reviews to capturing mental states. In this paper, we present a novel SA feature set; Emotional Variance Analysis (EVA), which captures patterns of emotional instability. Applying EVA on student journals garnered from an Experiential Learning (EL) course, we find that EVA is useful for profiling variations in sentiment polarity and intensity, which in turn can predict academic performance. As a feature set, EVA is compatible with a wide variety of Artificial Intelligence (AI) and Machine Learning (ML) applications. Although evaluated on education data, we foresee EVA to be useful in mental health profiling and consumer behaviour applications. EVA is available at https://qr.page/g/5jQ8DQmWQT4. Our results show that EVA was able to achieve an overall accuracy of 88.7% and outperform NLP (76.0%) and SentimentR (58.0%) features by 15.8% and 51.7% respectively when predicting student experiential learning grade scores through a Multi-Layer Perceptron (MLP) ML model.

SUBMITTER: Tan L

PROVIDER: S-EPMC9836260 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Emotional Variance Analysis: A new sentiment analysis feature set for Artificial Intelligence and Machine Learning applications.

Tan Leonard L Tan Ooi Kiang OK Sze Chun Chau CC Goh Wilson Wen Bin WWB

PloS one 20230112 1

Sentiment Analysis (SA) is a category of data mining techniques that extract latent representations of affective states within textual corpuses. This has wide ranging applications from online reviews to capturing mental states. In this paper, we present a novel SA feature set; Emotional Variance Analysis (EVA), which captures patterns of emotional instability. Applying EVA on student journals garnered from an Experiential Learning (EL) course, we find that EVA is useful for profiling variations ...[more]

PMID: 36634041

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Project description:The exponential rise in social media via microblogging sites like Twitter has sparked curiosity in sentiment analysis that exploits user feedback towards a targeted product or service. Considering its significance in business intelligence and decision-making, numerous efforts have been made in this area. However, lack of dictionaries, unannotated data, large-scale unstructured data, and low accuracies have plagued these approaches. Also, sentiment classification through classifier ensemble has been underexplored in literature. In this article, we propose a Semantic Relational Machine Learning (SRML) model that automatically classifies the sentiment of tweets by using classifier ensemble and optimal features. The model employs the Cascaded Feature Selection (CFS) strategy, a novel statistical assessment approach based on Wilcoxon rank sum test, univariate logistic regression assisted significant predictor test and cross-correlation test. It further uses the efficacy of word2vec-based continuous bag-of-words and n-gram feature extraction in conjunction with SentiWordNet for finding optimal features for classification. We experiment on six public Twitter sentiment datasets, the STS-Gold dataset, the Obama-McCain Debate (OMD) dataset, the healthcare reform (HCR) dataset and the SemEval2017 Task 4A, 4B and 4C on a heterogeneous classifier ensemble comprising fourteen individual classifiers from different paradigms. Results from the experimental study indicate that CFS supports in attaining a higher classification accuracy with up to 50% lesser features compared to count vectorizer approach. In Intra-model performance assessment, the Artificial Neural Network-Gradient Descent (ANN-GD) classifier performs comparatively better than other individual classifiers, but the Best Trained Ensemble (BTE) strategy outperforms on all metrics. In inter-model performance assessment with existing state-of-the-art systems, the proposed model achieved higher accuracy and outperforms more accomplished models employing quantum-inspired sentiment representation (QSR), transformer-based methods like BERT, BERTweet, RoBERTa and ensemble techniques. The research thus provides critical insights into implementing similar strategy into building more generic and robust expert system for sentiment analysis that can be leveraged across industries.

Dataset Information

Emotional Variance Analysis: A new sentiment analysis feature set for Artificial Intelligence and Machine Learning applications.

Publications

Emotional Variance Analysis: A new sentiment analysis feature set for Artificial Intelligence and Machine Learning applications.

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