scholarly journals Aspect Βased Classification Model for Social Reviews

2017 ◽  
Vol 7 (6) ◽  
pp. 2296-2302 ◽  
Author(s):  
J. Mir ◽  
A. Mahmood ◽  
S. Khatoon
Keyword(s):  
Opinion Mining ◽  
Naive Bayes ◽  
Word Identification ◽  
Naïve Bayes ◽  
Classification Model ◽  
Product Reviews ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Proposed Model

Aspect based opinion mining investigates deeply, the emotions related to one’s aspects. Aspects and opinion word identification is the core task of aspect based opinion mining. In previous studies aspect based opinion mining have been applied on service or product domain. Moreover, product reviews are short and simple whereas, social reviews are long and complex. However, this study introduces an efficient model for social reviews which classifies aspects and opinion words related to social domain. The main contributions of this paper are auto tagging and data training phase, feature set definition and dictionary usage. Proposed model results are compared with CR model and Naïve Bayes classifier on same dataset having accuracy 98.17% and precision 96.01%, while recall and F1 are 96.00% and 96.01% respectively. The experimental results show that the proposed model performs better than the CR model and Naïve Bayes classifier.

scholarly journals Opinion Mining Classification using Naive Bayes Algorithm

2020 ◽  
Vol 9 (5) ◽  
pp. 495-498
Keyword(s):  
Opinion Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Product Review ◽  
Online Services ◽  
Recent Advancement ◽  
Bayes Algorithm

With the recent advancement in the field of online services, the importance of a review for a product has also gone up. In this paper we focus on the aspect of reducing the time and effort for the user by recommending the best product to him. For this to be achieved, this paper proposes a Naive Bayes Classifier which labels the reviews accurately and combines the reviews to give a final rating to the product. The amazon product review data consisting of both negative and positive reviews was used for training and testing purposes. The model’s performance is evaluated, and results are analysed.

scholarly journals Solar Irradiance Forecast Using Naïve Bayes Classifier Based on Publicly Available Weather Forecasting Variables

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1529 ◽  
Author(s):  
Youngsung Kwon ◽  
Alexis Kwasinski ◽  
Andres Kwasinski
Keyword(s):  
Naive Bayes ◽  
Weather Forecasting ◽  
Weather Conditions ◽  
Naïve Bayes ◽  
Dew Point ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Forecast Performance ◽  
Proposed Model

This paper develops an approach for two-day-ahead global horizontal irradiance (GHI) forecast using the naïve Bayes classifier (NB). Based on publicly available weather forecasting information about temperature, relative humidity, dew point, and sky coverage, they are used as a training set in NB classification with hourly resolution. To reduce having two times with the same GHI affecting the classification in the proposed model, two characteristics of the GHI under different weather conditions are considered: The daylight variation and diurnal cycle. More importantly, NB’s independence assumption-based on simple Bayes’ theorem makes the process speed faster and less constrained than other classification algorithms. The forecast performance is verified with several error criteria from established analytical practices using relevant statistics. Moreover, commonly used forecasting error criteria are discussed. This NB model shows improved results regarding error criteria and a good agreement for a clear day that satisfies the guideline for the evaluation of two-days-ahead forecast, when compared with other recent techniques.

scholarly journals Penerapan Naïve Bayes Classifier dengan Gaussian Function Untuk Menentukan Kelompok UKT

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Reza Ade Putra
Keyword(s):  
Cross Validation ◽  
Naive Bayes ◽  
Gaussian Function ◽  
Naïve Bayes ◽  
Classification Model ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Tuition Fees ◽  
Naïve Bayes Classifier ◽  
Fold Cross Validation

<p class="SammaryHeader" align="center"><strong><em>Abstract</em></strong></p><p><em>Uang Kuliah Tunggal hereinafter abbreviated as UKT is part of a single tuition fees</em><em> incurred by each student in each department or study program for diploma and degree courses. UKT is the amount of fees to be paid by the student in each semester. </em><em>Basically, the purpose of UKT is to charge tuition fees according to income and family circumstances students.</em><em> However, there is a problem regarding the classification UKT improperly. It is caused by several factors, including determining UKT groups still use manual method, as well as the substance of subjectivity in the determination of a new student UKT  groups. Based on these problems, we need a decision support system that can help in determining the UKT group of new students. In applying UKT, Cot Kala IAIN Zawiyah Langsa split into 3 (three) categories UKT group. Naïve Bayes classifier methods is used to classify data into three UKT groups. Research results show that the results of validation testing of NBC classification model with a 3-fold cross validation generates an average accuracy of 86.67%. so that it can be concluded that the level of effectiveness of the UKT classification model with the NBC method is included in the fairly good category.</em></p><p><strong><em>Keyword</em></strong><strong><em>s : </em></strong><em>UKT Groups, Naive Bayes Classifier, K-fold cross validation</em><em></em></p><p><em> </em></p><p class="SammaryHeader" align="center"><strong><em>Abstra</em><em>k</em></strong></p><p><em>Uang Kuliah Tunggal yang selanjutnya disingkat UKT merupakan sebagian dari biaya</em><em> kuliah tunggal yang ditanggung oleh setiap mahasiswa pada setiap jurusan atau program studi untuk program diploma dan program sarjana. UKT merupakan besaran biaya yang harus dibayarkan oleh mahasiswa pada setiap semester. </em><em>Pada dasarnya, tujuan diberlakukannya UKT yaitu untuk membebankan biaya kuliah sesuai dengan penghasilan dan kondisi keluarga mahasiswa yang bersangkutan. Akan tetapi, terjadi permasalahan tentang penggolongan UKT yang tidak tepat. Ini disebabkan oleh beberapa faktor, diantaranya dalam menentukan kelompok UKT masih menggunakan cara manual, serta adanya unsur subjektivitas dalam penentuan kelompok UKT mahasiswa baru. Berdasarkan permasalahan tersebut, dibutuhkan suatu sistem pendukung keputusan yang dapat membantu dalam menentukan kelompok UKT mahasiswa baruDalam menerapkan Uang Kuliah Tunggal, IAIN Zawiyah Cot Kala Langsa membagi kedalam 3 (Tiga) kategori kelompok UKT . Metode Naïve Bayes Classifier digunakan untuk mengklasifikasikan data menjadi tiga kelompok UKT. Hasil penelitian menunjukkan bahwa hasil validasi pengujian model klasifikasi NBC dengan 3- fold cross validation menghasilkan rata-rata akurasi sebesar 86.67%, sehingga dapat disimpulkan bahwa tingkat efektivitas model klasifikasi UKT dengan metode NBC termasuk pada kategori cukup baik.</em><em>.</em></p><strong><em>Kata kunci : </em></strong><em>Kelompok UKT, Naive Bayes Classifier, K-fold cross validation</em>

scholarly journals 440. Detection of COVID-19 Patients Requiring Escalation to ICU Status Using a Naïve Bayes Classifier

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S322-S322
Author(s):  
William R Barnett ◽  
Chad Jaenke ◽  
Zachary Holtzapple ◽  
James Williams ◽  
Nithin Kesireddy ◽  
...  
Keyword(s):  
Predictive Value ◽  
Naive Bayes ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Naïve Bayes ◽  
Classification Model ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Chi Square Analysis

Abstract Background A naïve Bayes classifier is a popular tool used in assigning variables an equal and independent contribution to a binary decision. With respect to COVID-19 severity, the naïve Bayes classifier can consider different variables, such as age, gender, race/ethnicity, comorbidities, and initial laboratory values to determine the probability a patient may need to be admitted or transferred to an intensive care unit (ICU). The aim of this study was to develop a screening tool to detect COVID-19 patients that may require escalation to ICU status. Methods Patients hospitalized with COVID-19 were gathered from the end of March 2020 to the end of May 2020 from four hospitals in our metropolitan area. We began searching for potential variables to include in the classification model using chi-square analysis or calculating the optimal cutpoint to separate ICU and non-ICU status. After identifying significant variables, we began using standard procedures to construct a classifier. The dataset was split 7:3 to create samples for training and testing. To appraise the model’s performance, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the curve (AUC), and the Matthew’s correlation coefficient (MCC) were calculated. Table 1. Univariate analysis of variables in the COVID-19 dataset dichotomized by ICU status Results A total of 574 COVID-19 patients were included in the study. There were 402 patients in the training sample and 172 patients in the testing sample. The naïve Bayes classifier demonstrated an overall accuracy result of 75.6% (95% CI; 68.5% – 81.8%) using the 14 variables listed in Table 1. The model was able to correctly classify 84.9% of ICU status patients (sensitivity), but only 54.7% of non-ICU status patients (specificity). The PPV and the NPV were 80.1% and 61.7%, respectively. The AUC was 0.717 (95% CI; 0.629 – 0.805) and the MCC was 0.410. Conclusion Our naïve Bayes classifier operates by recognizing certain aspects of severe COVID-19 cases and looking for the probability of the variables in said patients. We present a classification model that potentially could be used alongside other tools to screen patients with COVID-19 early in their hospital course to identify those needing escalation to ICU level care. Disclosures All Authors: No reported disclosures

scholarly journals Polarity Detection on Real-Time News Data Using Opinion Mining

2020 ◽  
Author(s):  
Boppuru Rudra Prathap ◽  
Sujatha A K ◽  
Chandragiri Bala Satish Yadav ◽  
Mummadi Mounika
Keyword(s):  
Machine Learning ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
News Article ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Knowledge Based ◽  
The Given

Sentimental Analysis or Opinion Mining plays a vital role in the experimentation field that determines the user’s opinions, emotions and sentiments concealing a text. News on the Internet is becoming vast, and it is drawing attention and has reached the point of adequately affecting political and social realities. The popular way of checking online content, i.e. manual knowledge-based on the facts, is practically impossible because of the enormous amount of data that has now generated online. The issue can address by using Machine Learning Algorithms and Artificial Intelligence. One of the Machine Learning techniques used in this is Naive Bayes classifier. In this paper, the polarity of the news article determined whether the given news article is a positive, negative or neutral Naive Bayes Classifier, which works well with NLP (Natural Language problems) used for many purposes. It is a family of probabilistic algorithms that used to identify a word from a given text. In this, we calculate the probability of each word in a given text. Using Bayes theorem, they are getting the probabilities based on the given conditions. Topic Modeling is analytical modelling for finding the abstract of topics from a cluster of documents. Latent Dirichlet Allocation (LDA) is a topic model is used to classify the text in a given document to a specified topic. The news article is classified as positive or negative or neutral using Naive Bayes classifier by calculating the probabilities of each word from a given news article. By using topic modelling (LDA), topics of articles are detected and record data separately. The calculation of the overall sentiment of a chosen topic from different newspapers from previously recorded data done.

scholarly journals Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm

2019 ◽  
Vol 2 (2) ◽  
pp. 48-55
Author(s):  
Taqwa Hariguna ◽  
Wiga Maulana Baihaqi ◽  
Aulia Nurwanti
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Product Reviews ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Product Review ◽  
Negative Comment ◽  
Negative Comments

In an e-commerce Shopee, the process of selling and buying continues to run every day, and the comments given by consumers will increase more and more. Comments given by consumers will be the reference/review of a product that has been purchased by consumers. Consumers freely provide a review containing positive comments and negative comments in the Comments field listed on the Shopee e-commerce website. With the above problems, researchers will do a research with the method of sentiment analysis to distinguish classes in product review comments that include positive comment class or negative comment class using a combination of K-means and naive Bayes classifier. K-means used to determine the grouping of classes; naive Bayes classifier used to get the value of accuracy. The results obtained based on clustering K-means include getting 116 negative comments on product reviews and 37 negative comments product reviews. Accuracy results obtained from product review comment data of 77.12%. Thus, the accuracy value using K-means and naive Bayes classifier without manual data get a higher accuracy value is compared using K-means, Naive Bayes classifier, and manual data get results lower accuracy of 56.86%. From the results above the most comments is a negative comment of 116 data review comments product, from the results of the study can be concluded that one of the products of Spatuafa named high heels women know the Ribbon Ikat FX18 the condition of the product is not good enough due to the high negative comments compared to positive comments

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