Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble

Abstract

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.

A Parallel Approach of Cascade Modelling Using MPI4Py on Imbalanced Dataset

“…These classifiers divide the data flow into multiple stages, with each focusing on a specific aspect of the classification task. This method has significantly improved the overall recognition accuracy in various tasks, such as physical activity recognition [22], sentiment analysis of tweets [23], and weather forecasting [24]. The structure of cascade classifiers enhances generalization performance and stability in classification tasks, surpassing conventional single classifiers.…”

Graph convolution networks for social media trolls detection use deep feature extraction

“…Interpretation of the extracted text often employs machine learning and deep learning techniques, commonly used in natural language processing (NLP) tasks like sentiment detection. Various models, including Support Vector Machine (SVM) and Extreme Machine Learning (ELM), have been applied to this task, showing high success rates in classifying texts [37][38][39][40]. Research on the classification of online toxic comments has explored standard machine learning algorithms applied to datasets comprising different types of toxicity.…”

SentiMLBench: Benchmark Evaluation of Machine Learning Algorithms for Sentiment Analysis

“…We use the Semantic Relational Machine Learning (SRML) model developed by [27] for comparative performance evaluation of classifiers both as base classification techniques and implement the ensemble classifier. Four datasets as shown in Table 3…”