A Comparative Study on New Classification Algorithm using NASA MDP Datasets for Software Defect Detection

Sreedevi, E. P.; Premalatha, V.; Sivakumar, S.; Nayak, Soumya Ranjan

doi:10.1109/iss1.2019.8908096

Cited by 13 publications

(2 citation statements)

References 10 publications

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“…2) Pre-processing: first processing step, usually done by tokenization process, elimination of stop words and stemming process [11].…”

Section: Sentimental Analysismentioning

confidence: 99%

Feature-Based Sentimental Analysis On Product Review System Using CUDA-BB Algorithm

2020

IJETER

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Sentiment analysis methods are classified as feature extraction method, i.e., view's, review's or sentence to predict the emotion of a sentence or a text using natural languages processing(NLP). The analysis of sentiment involves classification of the text into three phases "positive," "negative" or "The Neutral." It Analyzes the data and labels and classified as either "good" "better" "best" or "bad", "worse" based on the emotions or feeling given by customer and finally classified as positive and negative or neutral respectively. So, in regard for the past few years, The World Wide Web (WWW) has become a vast source in providing raw data which is is in the form of opinions or emotions or reviews given by the user or customer about a particular product. E -commerce, has grabbed the attention of the business people to improve the quality of their product by taking the review's from different Social media websites like Facebook , Twitter, Amazon, Flipkart, etc Sentimental analysis or Opinion mining is one of the major challenges of NLP (natural language processing).Business Analytics plays a very important role in the current scenario. In particular, these people rely on the feedback of their products given by the customer's to withstand the competition and knowledge mining that can give them an outstanding view of what to expect in the future..Classification and rule induction describes the main topics in the field of decision making and knowledge discovery. In this paper, we propose CUDABB (CUDA Bag-Boost) algorithm is used to find the overall star rating of Smartphone's using GPU parallel computing. This strategy not only reduces variance and bias but also this approach allows to produce better predictive performance compared to a single model. And the results obtained is compared with SLIQ and MMDBM using pycuda and GPU with computed acceleration rate(speedup) time using Amazon mobile review dataset. The aim of GPU mining technique is to enhance the execution speed with less handling time. Finally, we conclude that the proposed method achieves better accuracy.

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“…2) Pre-processing: first processing step, usually done by tokenization process, elimination of stop words and stemming process [11].…”

Section: Sentimental Analysismentioning

confidence: 99%

Feature-Based Sentimental Analysis On Product Review System Using CUDA-BB Algorithm

2020

IJETER

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“…One of the fundamental issues of Natural Language Processing (NLP) is analyzing an emotional representation or interpretation of an opinion [ 5 ]. For example, based on the ideas and feedback received from many individuals, entrepreneurs can understand how to sustain themselves in the market and make a vision for the future [ 6 ]. As a result, these individuals seek to develop their market or website by providing the highest quality information.…”

Section: Introductionmentioning

confidence: 99%

Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model

Pathuri

Anbazhagan

Joshi

et al. 2021

Sensors

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The COVID-19 pandemic has spread to almost all countries of the World and affected people both mentally and economically. The primary motivation of this research is to construct a model that takes reviews or evaluations from several people who are affected with COVID-19. As the number of cases has accelerated day by day, people are becoming panicked and concerned about their health. A good model may be helpful to provide accurate statistics in interpreting the actual records about the pandemic. In the proposed work, for sentimental analysis, a unique classifier named the Sentimental DataBase Miner algorithm (SADBM) is used to categorize the opinions and parallel processing, and is applied on the data collected from various online social media websites like Twitter, Facebook, and Linkedin. The accuracy of the proposed model is validated with trained data and compared with basic classifiers, such as logistic regression and decision tree . The proposed algorithm is executed on CPU as well as GPU and calculated the acceleration ratio of the model. The results show that the proposed model provides the best accuracy compared with the other two models, i.e., 96% (GPU).

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Agile Methodology for IoT Application Development and Business Improvisation

Pandit

Patnaik

et al. 2021

Lecture Notes in Networks and Systems

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A Comparative Study on New Classification Algorithm using NASA MDP Datasets for Software Defect Detection

Cited by 13 publications

References 10 publications

Feature-Based Sentimental Analysis On Product Review System Using CUDA-BB Algorithm

Feature-Based Sentimental Analysis On Product Review System Using CUDA-BB Algorithm

Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model

Agile Methodology for IoT Application Development and Business Improvisation

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