Text Clustering and Classification Techniques- A Review

Text classification is the task of automatically sorting a set of documents into categories from a predefined set. Text Classification is a data mining technique used to predict group membership for data instances within a given dataset. It is used for classifying data into different classes by considering some constrains. Instead of traditional feature selection techniques used for text document classification. A Naive Bayesian model is easy to build, with no complicated iterative parameter estimation which makes it particularly useful for very large datasets. Automated Text categorization and class prediction is important for text categorization to reduce the feature size and to speed up the learning process of classifiers

Text Clustering and Classification Techniques using Data Mining

Text classification is the task of automatically sorting a set of documents into categories from a predefined set. Text Classification is a data mining technique used to predict group membership for data instances within a given dataset. It is used for classifying data into different classes by considering some constrains. Instead of traditional feature selection techniques used for text document classification. A Naive Bayesian model is easy to build, with no complicated iterative parameter estimation which makes it particularly useful for very large datasets. Automated Text categorization and class prediction is important for text categorization to reduce the feature size and to speed up the learning process of classifiers

Text Catagorization Using Hybrid Na�ve Bayes Algorithm

Automated Text categorization and class prediction is important for text categorization to reduce the feature size and to speed up the learning process of classifiers .Text classification is a growing interest in the research of text mining. Correctly identifying the Text into particular category is still presenting challenge because of large and vast amount of features in the dataset. In regards to the present classifying approaches, Na�ve Bayes is probably smart at serving as a document classification model thanks to its simplicity. The aim of this Project is to spotlight the performance of Text categorization and sophistication prediction Na�ve Bayes in Text classification

An Overview on Implementation Using Hybrid Na�ve Bayes Algorithm for Text Categorization

Automated Text categorization and class prediction is important for text categorization to reduce the feature size and to speed up the learning process of classifiers .Text classification is a growing interest in the research of text mining. Correctly identifying the Text into particular category is still presenting challenge because of large and vast amount of features in the dataset. In regards to the present classifying approaches, Na�ve Bayes is probably smart at serving as a document classification model thanks to its simplicity. The aim of this Project is to spotlight the performance of Text categorization and sophistication prediction Na�ve Bayes in Text classification

Role of sentiment classification in sentiment analysis: a survey

Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results

Transfer Learning using Computational Intelligence: A Survey

Abstract Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. In contrast to classical machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling consisting of different data patterns in the current domain. To improve the performance of existing transfer learning methods and handle the knowledge transfer process in real-world systems, ..