Exploring Public Sentiment: A Sentiment Analysis of GST Discourse on Twitter using Supervised Machine Learning Classifiers

A key economic move that resulted in heated disputes was India's introduction of the Goods and Services Tax (GST). Social media channels offered a widely used forum for the people to express their views on the GST, providing insightful data for gauging mood and guiding next revisions. The emotion of 5629 GST-related tweets was assessed using the VADER lexicon after being obtained using the Twitter Developer API. The tf-idf feature was used for text vectorization, with 80% of the data going toward training and the remaining 20% going toward testing. In this study, six well-known classifiers—the Ridge Classifier, Logistic Regression, Linear SVC, Perceptron, Decision Tree, and K-Nearest Neighbor—were thoroughly compared to evaluate their performance in a range of circumstances. Accuracy, precision, recall, f-score, training, and testing times were all included in the performance measurements. The study presented novel pre-processing methods and examined the training/testing times before coming to the conclusion that the Ridge Classifier performed better than the others in terms of accuracy, precision, and efficiency. In this study, six well-known classifiers—the Ridge Classifier, Logistic Regression, Linear SVC, Perceptron, Decision Tree, and K-Nearest Neighbor—were thoroughly compared to evaluate their performance in a range of circumstances. Accuracy, precision, recall, f-score, training, and testing times were all included in the performance measurements. The study presented novel pre-processing methods and examined the training/testing times before coming to the conclusion that the Ridge Classifier performed better than the others in terms of accuracy, precision, and efficiency

Sentiment Classification Using a Sense Enriched Lexicon-based Approach

The prominent approach in sentiment polarity classification is the Lexicon-based approach which relies on a dictionary to assign a score to subjective words. Most of the existing work use score of the most dominant sense in this process instead of using the contextually appropriate sense. The use of Word Sense Disambiguation (WSD) is less investigated in the sentiment classification tasks. This paper investigates the effect of integrating WSD into a Lexicon-based approach for Sentiment Polarity classification and compares it with the existing Lexicon-based approaches and the state-of-art supervised approaches. The lexicon used in this work is SentiWordNet v2.0. The proposed approach, called Sense Enriched Lexicon-based Approach (SELSA), uses a word sense disambiguation module to identify the correct sense of subjective words. Instead of using the score of the most frequent sense, it uses the score of the contextually appropriate sense only. For the purpose of comparison with the supervised approaches, the authors investigate Naïve Bayes (NB) and Support Vector Machines (SVM) classifiers which tend to perform better in earlier research. The performance of these classifiers is evaluated using Word2vec, Hashing Vectorizer, and bi-gram feature. The best-performing classifier-feature combination is used for comparison. All the evaluations are done on the Movie Review dataset. SELSA achieves an accuracy of 96.25% which is significantly better than the accuracy obtained by SentiWordNet-based approach without WSD on the same dataset. The performance of the proposed algorithm is also compared with the best-performing supervised classifier investigated in this work and earlier reported works on the same dataset. The results reveal that the SVM classifier performs better than SentiWordNet approach without WSD. However, after incorporating WSD the performance of the proposed Lexicon-based approach is significantly improved and it surpasses the best-performing supervised classifier (SVM with bi-gram features)

Genotyping and drug resistance patterns of M. tuberculosis strains in Pakistan

<p>Abstract</p> <p>Background</p> <p>The incidence of tuberculosis in Pakistan is 181/100,000 population. However, information about transmission and geographical prevalence of <it>Mycobacterium tuberculosis </it>strains and their evolutionary genetics as well as drug resistance remains limited. Our objective was to determine the clonal composition, evolutionary genetics and drug resistance of <it>M. tuberculosis </it>isolates from different regions of the country.</p> <p>Methods</p> <p><it>M. tuberculosis </it>strains isolated (2003–2005) from specimens submitted to the laboratory through collection units nationwide were included. Drug susceptibility was performed and strains were spoligotyped.</p> <p>Results</p> <p>Of 926 <it>M. tuberculosis </it>strains studied, 721(78%) were grouped into 59 "shared types", while 205 (22%) were identified as "Orphan" spoligotypes. Amongst the predominant genotypes 61% were Central Asian strains (CAS ; including CAS1, CAS sub-families and Orphan Pak clusters), 4% East African-Indian (EAI), 3% Beijing, 2% poorly defined TB strains (T), 2% Haarlem and LAM (0.2). Also TbD1 analysis (<it>M. tuberculosis </it>specific deletion 1) confirmed that CAS1 was of "modern" origin while EAI isolates belonged to "ancestral" strain types.</p> <p>Prevalence of CAS1 clade was significantly higher in Punjab (P < 0.01, Pearsons Chi-square test) as compared with Sindh, North West Frontier Province and Balochistan provinces. Forty six percent of isolates were sensitive to five first line antibiotics tested, 45% were Rifampicin resistant, 50% isoniazid resistant. MDR was significantly associated with Beijing strains (P = 0.01, Pearsons Chi-square test) and EAI (P = 0.001, Pearsons Chi-square test), but not with CAS family.</p> <p>Conclusion</p> <p>Our results show variation of prevalent <it>M. tuberculosis </it>strain with greater association of CAS1 with the Punjab province. The fact that the prevalent CAS genotype was not associated with drug resistance is encouraging. It further suggests a more effective treatment and control programme should be successful in reducing the tuberculosis burden in Pakistan.</p

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries