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

Development of a patients' satisfaction analysis system using machine learning and lexicon-based methods

BACKGROUND

Patients' rights are integral to medical ethics. This study aimed to perform sentiment analysis and opinion mining on patients' messages by a combination of lexicon-based and machine learning methods to identify positive or negative comments and to determine the different ward and staff names mentioned in patients' messages.

METHODS

The level of satisfaction and observance of the rights of 250 service recipients of the hospital was evaluated through the related checklists by the evaluator. In total, 822 Persian messages, composed of 540 negative and 282 positive comments, were collected and labeled by the evaluator. Pre-processing was performed on the messages and followed by 2 feature vectors which were extracted from the messages, including the term frequency-inverse document frequency (TFIDF) vector and a combination of the multifeature (MF) (a lexicon-based method) and TFIDF (MF + TFIDF) vectors. Six feature selectors and 5 classifiers were used in this study. For the evaluations, 5-fold cross-validation with different metrics including area under the receiver operating characteristic curve (AUC), accuracy (ACC), F1 score, sensitivity (SEN), specificity (SPE) and Precision-Recall Curves (PRC) were reported. Message tag detection, which featured different hospital wards and identified staff names mentioned in the study patients' messages, was implemented by the lexicon-based method.

RESULTS

The best classifier was Multinomial Naïve Bayes in combination with MF + TFIDF feature vector and SelectFromModel (SFM) feature selection (ACC = 0.89 ± 0.03, AUC = 0.87 ± 0.03, F1 = 0.92 ± 0.03, SEN = 0.93 ± 0.04, and SPE = 0.82 ± 0.02, PRC-AUC = 0.97). Two methods of assessment by the evaluator and artificial intelligence as well as survey systems were compared.

CONCLUSION

Our results demonstrated that the lexicon-based method, in combination with machine learning classifiers, could extract sentiments in patients' comments and classify them into positive and negative categories. We also developed an online survey system to analyze patients' satisfaction in different wards and to remove conventional assessments by the evaluator.

AirBERT: A fine-tuned language representation model for airlines tweet sentiment analysis

Airlines operate in a competitive marketplace and must upgrade their services to meet customer safety and comfort. Post-pandemic, the government and airlines resumed flights with many restrictions, the impact which is unexplored. An increasing number of customers use social media to leave reviews and in this age of Machine Learning (ML), if a model is available to automatically polarize flyer sentiments, it can help airlines upscale. In this work, a custom dataset is scraped from Twitter by including online reviews of five Indian airlines. Multiclass sentiment analysis using three classifiers, support vector machine, K-nearest neighbor and random forest with word2vec and TF-IDF word embeddings is implemented. AirBERT, a fine-tuned deep learning attention model based on bidirectional encoder representation from transformers is proposed. From results, it is observed that on ML, Random Forest with TF-IDF performs the best but the graphical processing unit and domain corpora trained AirBERT outperforms all the other models with an accuracy of 91%. Indigo airlines and Jet Airways received the maximum percentage of positive and negative reviews respectively. In performance comparison with three existing models on the USA airlines tweets dataset, the proposed model outperforms others trained on general domain corpora and matches state-of-the-art TweetBERTv2 model accuracy. The model can be deployed by airlines and other service industries to implement a customer relationship management (CRM) system.