Opinion mining framework using proposed RB-bayes model for text classication

Bhalla, Rajni; Bagga, Amandeep

doi:10.11591/ijece.v9i1.pp477-484

Cited by 12 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…But unable to handle the problem of the likelihood of zero possibility. RB-Bayes is proposed to solve this problem [23]. RB-Bayes algorithm provides a way of calculating prediction.…”

Section: Proposed Rb-bayes Algorithmmentioning

confidence: 99%

“…To make life better, we need to record history about the patient, so that it will help the doctors to take the best decision for patient's health. The major goal of this paper is to create such an algorithm that creates a group of patterns and naturally settle on choice in light of preparing information [6]. To check validation of the algorithm, we are testing on the real-time dataset.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

RB-Bayes algorithm for the prediction of diabetic in Pima Indian dataset

Rajni

Amandeep

2019

IJECE

View full text Add to dashboard Cite

<p>Diabetes is a major concern all over the world. It is increasing at a fast pace. People can avoid diabetes at an early stage without any test. The goal of this paper is to predict the probability of whether the person has a risk of diabetes or not at an early stage. This would lead to having a great impact on their quality of human life. The datasets are Pima Indians diabetes and Cleveland coronary illness and consist of 768 records. Though there are a number of solutions available for information extraction from a huge datasets and to predict the possibility of having diabetes, but the accuracy of their mining process is far from accurate. For achieving highest accuracy, the issue of zero probability which is generally faced by naïve bayes analysis needs to be addressed suitably. The proposed framework RB-Bayes aims to extract the required information with high accuracy that could survive the problem of zero probability and also configure accuracy with other methods like Support Vector Machine, Naive Bayes, and K Nearest Neighbor. We calculated mean to handle missing data and calculated probability for yes (positive) and no (negative). The highest value between yes and no decide the value for the tuple. It is mostly used in text classification. The outcomes on Pima Indian diabetes dataset demonstrate that the proposed methodology enhances the precision as a contrast with other regulated procedures. The accuracy of the proposed methodology large dataset is 72.9%.</p>

show abstract

“…But unable to handle the problem of the likelihood of zero possibility. RB-Bayes is proposed to solve this problem [23]. RB-Bayes algorithm provides a way of calculating prediction.…”

Section: Proposed Rb-bayes Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RB-Bayes algorithm for the prediction of diabetic in Pima Indian dataset

Rajni

Amandeep

2019

IJECE

View full text Add to dashboard Cite

show abstract

“…However, the application of classification of textual content is frequently used in natural language processing. The application associated with text classification assists in looking for the data that explicitly exists in the text given in natural language processing [9]. This approach assists in extracting the level of standard emotions that resides within every terms present in corpus of feedback or opinion shared by customers [8].…”

Section: Introductionmentioning

confidence: 99%

“…All these application are developed using various available libraries e.g. Apache OpenNLP, Natural Language Toolkit, Stanford NLP suite etc [9]. However, the domain of natural language processing is yet to see its superior accuracy.…”

Section: Introductionmentioning

confidence: 99%

Modelling Simple and Efficient Data Transformation Scheme for Improving Natural Language Processing

J*,

Swamy

2020

IJITEE

View full text Add to dashboard Cite

The importance of natural language processing cannot be sided in the current era of communication and analytics where data is exponentially growing. Although, there has been various versions and schemes that has evolved in the past decade towards improving the performance of natural language processing, but still the problem towards precisely extracting the actual context is an open ended. Review of existing studies show a large scope of new work towards improving it. Therefore, this manuscript presents a unique simplified approach where natural language processing is carried out with a combined effort of syntactical and semantic based transformation scheme. The study is implemented using analytical methodology while the secondary motive of the work is also to balance the mining performance as well as optimizing storage performance too. The study outcome shows proposed scheme to excel better performance with respect to time duration in all the internal processes being involved for data transformation.

show abstract

“…The work of Bhalla and Bagga [32] introduces a model known as opinion mining for the text categorization. This model has been developed by using RB-bayes model.…”

Section: Introductionmentioning

confidence: 99%

Framework for efficient transformation for complex medical data for improving analytical capability

Chandru

Seetharam²

2020

IJECE

View full text Add to dashboard Cite

The adoption of various technological advancement has been already adopted in the area of healthcare sector. This adoption facilitates involuntary generation of medical data that can be autonomously programmed to be forwarded to a destined hub in the form of cloud storage units. However, owing to such technologies there is massive formation of complex medical data that significantly acts as an overhead towards performing analytical operation as well as unwanted storage utilization. Therefore, the proposed system implements a novel transformation technique that is capable of using a template based stucture over cloud for generating structured data from highly unstructured data in a non-conventional manner. The contribution of the propsoed methodology is that it offers faster processing and storage optimization. The study outcome also proves this fact to show propsoed scheme excels better in performance in contrast to existing data transformation scheme.

show abstract

Opinion mining framework using proposed RB-bayes model for text classication

Cited by 12 publications

References 18 publications

RB-Bayes algorithm for the prediction of diabetic in Pima Indian dataset

RB-Bayes algorithm for the prediction of diabetic in Pima Indian dataset

Modelling Simple and Efficient Data Transformation Scheme for Improving Natural Language Processing

Framework for efficient transformation for complex medical data for improving analytical capability

Contact Info

Product

Resources

About