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An Ensemble Based Approach for Sentiment Classification in Asian Regional Language
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作者 Mahesh B.Shelke jeong gon lee +4 位作者 Sovan Samanta Sachin N.Deshmukh G.Bhalke Daulappa Rahul B.Mannade Arun Kumar Sivaraman 《Computer Systems Science & Engineering》 SCIE EI 2023年第3期2457-2468,共12页
In today’s digital world,millions of individuals are linked to one another via the Internet and social media.This opens up new avenues for information exchange with others.Sentiment analysis(SA)has gotten a lot of at... In today’s digital world,millions of individuals are linked to one another via the Internet and social media.This opens up new avenues for information exchange with others.Sentiment analysis(SA)has gotten a lot of attention during the last decade.We analyse the challenges of Sentiment Analysis(SA)in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which wefirst produced an annotated dataset composed of Marathi text acquired from microblogging websites such as Twitter.We also choose domain experts to manually annotate Marathi microblogging posts with positive,negative,and neutral polarity.In addition,to show the efficient use of the annotated dataset,an ensemble-based model for sentiment analysis was created.In contrast to others machine learning classifier,we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation(cv),outcomes as 97.77%,f-score is 97.89%. 展开更多
关键词 Sentiment analysis machine learning lexical resource ensemble classifier
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Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network
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作者 Dhanalakshmi R Sovan Samanta +4 位作者 Arun Kumar Sivaraman jeong gon lee Balasundaram A Sanamdikar Sanjay Tanaji Priya Ravindran 《Computer Systems Science & Engineering》 SCIE EI 2023年第3期1939-1950,共12页
Fuzzy sets have undergone several expansions and generalisations in the literature,including Atanasov’s intuitionistic fuzzy sets,type 2 fuzzy sets,and fuzzy multisets,to name a few.They can be regarded as fuzzy mult... Fuzzy sets have undergone several expansions and generalisations in the literature,including Atanasov’s intuitionistic fuzzy sets,type 2 fuzzy sets,and fuzzy multisets,to name a few.They can be regarded as fuzzy multisets from a formal standpoint;nevertheless,their interpretation differs from the two other approaches to fuzzy multisets that are currently available.Hesitating fuzzy sets(HFS)are very useful if consultants have hesitation in dealing with group decision-making problems between several possible memberships.However,these possible memberships can be not only crisp values in[0,1],but also interval values during a practical evaluation process.Hesitant bipolar valued fuzzy set(HBVFS)is a generalization of HFS.This paper aims to introduce a general framework of multi-attribute group decision-making using social network.We propose two types of decision-making processes:Type-1 decision-making process and Type-2 decision-making process.In the Type-1 decision-making process,the experts’original opinion is proces for thefinal ranking of alternatives.In Type-2 decision making processs,there are two major aspects we consider.First,consistency tests and checking of consensus models are given for detecting that the judgments are logically rational.Otherwise,the framework demands(partial)decision-makers to review their assessments.Second,the coherence and consensus of several HBVFSs are established forfinal ranking of alternatives.The proposed framework is clarified by an example of software packages selection of a university. 展开更多
关键词 Group decision-making aggregation operators hesitant bipolar-valued fuzzy set
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Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction
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作者 S.Karthik Robin Singh Bhadoria +5 位作者 jeong gon lee Arun Kumar Sivaraman Sovan Samanta A.Balasundaram Brijesh Kumar Chaurasia S.Ashokkumar 《Computers, Materials & Continua》 SCIE EI 2022年第7期243-259,共17页
Data is always a crucial issue of concern especially during its prediction and computation in digital revolution.This paper exactly helps in providing efficient learning mechanism for accurate predictability and reduc... Data is always a crucial issue of concern especially during its prediction and computation in digital revolution.This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication.It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data.The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means.The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data.The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables.This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach.It also demonstrates the generative function forKalman-filer based prediction model and its observations.This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration(CPE)for Python. 展开更多
关键词 Bayesian learning model kalman filter machine learning data accuracy prediction
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