Machine learning classifiers: Evaluation of the performance in online reviews

This paper aims to evaluate the performance of the machine learning classifiers and identify the most suitable classifier for classifying sentiment value. The term “sentiment value” in this study is referring to the polarity (positive, negative or neutral) of the text. This work applies machine learning classifiers from WEKA (Waikato Environment for Knowledge Analysis) toolkit in order to perform their evaluation. WEKA toolkit is a great set of tools for data mining and classification. The performance of the machine learning classifiers was measured by examining overall accuracy, recall, precision, kappa statistic and applying few visualization techniques. Finally, the analysis is applied to find the most suitable classifier for classifying sentiment value. Results show that two classifiers from Rules and Trees categories of classifiers perform equally best comparing to the other classifiers from categories, such as Bayes, Functions, Lazy and Meta. This paper explores the performance of machine learning classifiers in sentiment value classification in the online reviews. Data used is never been used before to explore the performance of machine learning classifiers

Symmetric kappa as a function of unweighted kappas

It is shown that a symmetric kappa corresponding to a c x c table with c>2 categories can be written as a function of the unweighted kappa corresponding to the same table and the c(c - 1)/2 distinct unweighted kappas associated with the (c - 1) x (c - 1) tables that are obtained by combining two categories. The result is a new MGB-type result

Kappa coefficients for dichotomous-nominal classifications

Two types of nominal classifications are distinguished, namely regular nominal classifications and dichotomous-nominal classifications. The first type does not include an 'absence' category (for example, no disorder), whereas the second type does include an 'absence' category. Cohen's unweighted kappa can be used to quantify agreement between two regular nominal classifications with the same categories, but there are no coefficients for assessing agreement between two dichotomous-nominal classifications. Kappa coefficients for dichotomous-nominal classifications with identical categories are defined. All coefficients proposed belong to a one-parameter family. It is studied how the coefficients for dichotomous-nominal classifications are related and if the values of the coefficients depend on the number of categories. It turns out that the values of the new kappa coefficients can be strictly ordered in precisely two ways. The orderings suggest that the new coefficients are measuring the same thing, but to a different extent. If one accepts the use of magnitude guidelines, it is recommended to use stricter criteria for the new coefficients that tend to produce higher values

Kappa Coefficients for Circular Classifications

Circular classifications are classification scales with categories that exhibit a certain periodicity. Since linear scales have endpoints, the standard weighted kappas used for linear scales are not appropriate for analyzing agreement between two circular classifications. A family of kappa coefficients for circular classifications is defined. The kappas differ only in one parameter. It is studied how the circular kappas are related and if the values of the circular kappas depend on the number of categories. It turns out that the values of the circular kappas can be strictly ordered in precisely two ways. The orderings suggest that the circular kappas are measuring the same thing, but to a different extent. If one accepts the use of magnitude guidelines, it is recommended to use stricter criteria for circular kappas that tend to produce higher values

Diffusion-Weighted MR Enterography to Monitor Bowel Inflammation after Medical Therapy in Crohn's Disease: A Prospective Longitudinal Study

Objective: To prospectively evaluate the performance of diffusion-weighted imaging (DWI) to monitor bowel inflammation after medical therapy for Crohn's disease (CD). Materials and methods: Before and following 1-2 years of medical therapy, between October 2012 and May 2015, 18 randomly selected adult CD patients (male:female, 13:5; mean age ± SD, 25.8 ± 7.9 years at the time of enrollment) prospectively underwent MR enterography (MRE) including DWI (b = 900 s/mm2) and ileocolonoscopy. Thirty-seven prospectively defined index lesions (one contiguous endoscopy-confirmed inflamed area chosen from each inflamed anatomical bowel segment; 1-4 index lesions per patient; median, 2 lesions) were assessed on pre- and post-treatment MRE and endoscopy. Visual assessment of treatment responses on DWI in 4 categories including complete remission and reduced, unchanged or increased inflammation, and measurements of changes in apparent diffusion coefficient (ΔADC), i.e., pre-treatment-post-treatment, were performed by 2 independent readers. Endoscopic findings and CD MRI activity index (CDMI) obtained using conventional MRE served as reference standards. Results: ΔADC significantly differed between improved (i.e., complete remission and reduced inflammation) and unimproved (i.e., unchanged or increased inflammation) lesions: mean ± SD (× 10-3 mm2/s) of -0.65 ± 0.58 vs. 0.06 ± 0.15 for reader 1 (p = 0.022) and -0.68 ± 0.56 vs. 0.10 ± 0.26 for reader 2 (p = 0.025). DWI accuracy for diagnosing complete remission or improved inflammation ranged from 76% (28/37) to 84% (31/37). A significant negative correlation was noted between ΔADC and ΔCDMI for both readers with correlation coefficients of -0.438 and -0.461, respectively (p < 0.05). Conclusion: DWI is potentially a feasible tool to monitor quantitatively and qualitatively bowel inflammation of CD after medical treatment.ope