Book ChapterDOI
Optimization Scheme for Text Classification Using Machine Learning Naïve Bayes Classifier
Venkatesh,K. V. Ranjitha,B. S. Venkatesh Prasad +2 more
- pp 576-586
TLDR
A naive bayes classifier which scales directly with number of indicators and data points which can be used for both binary and multi-class classification problems, which demonstrates the performance improvement in the classification technique.Abstract:
Text classification is an essential advance in characteristic dialect processing. It very well may be performed utilizing different classification algorithms. Hadoop Map Reduce is widely utilized in text classification to perform classification on colossal measure of text data. However, Map Reduce required a ton of time to perform the tasks thereby increasing latency and since the data is distributed over the cluster it builds time and thus reducing processing speed. Also, Hadoop utilizes long queue of code. Motivated by this, we propose a basic yet compelling machine learning method which uses Naive Bayes classifier for text data. In Machine Learning approach, the classifier is built automatically by learning the properties of categories from a set of predefined training data. Hence, it can process complex furthermore, multi assortmentinformation in dynamic situations. Here we propose a naive bayes classifier which scales directly with number of indicators and data points which can be used for both binary and multi-class classification problems. We implemented the presented schemes using Machine Learning tool. The experimental results demonstrate the performance improvement in the classification technique.read more
Citations
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Proceedings ArticleDOI
Sentiment Analysis and Classification of Restaurant Reviews using Machine Learning
TL;DR: In this article, the authors analyzed the customer reviews about various restaurants across Karachi - one of the biggest cities of Pakistan, by using text categorization techniques, comments are automatically classified according to feedback about food taste, ambiance, service, and value for money.
Journal ArticleDOI
An efficient approach for textual data classification using deep learning
Abdullah Alqahtani,Habib Ullah Khan,Shtwai Alsubai,Mohemmed Sha,Ahmad Almadhor,Tayyab Iqbal,Sidra Abbas +6 more
TL;DR: This paper employs machine and deep learning techniques to classify textual data, and reveals that LSTM achieves 92% accuracy outperforming all other model and baseline studies.
Proceedings ArticleDOI
C4.5 and Naive Bayes for Sentiment Analysis Indonesian Tweet on E-Money User During Pandemic
TL;DR: In this article, the authors presented classification performance results for the C4.5 decision tree and Naive Bayes classifiers and compared each classifier's output using the voting-by-majority method to final predict positive or negative opinions about e-money in Indonesia.
Journal ArticleDOI
Text Classification Using Genetic Programming with Implementation of Map Reduce and Scraping
Wirarama Wedashwara,Budi Irmawati,Heri Wijayanto,I Wayan Agus Arimbawa,Vandha Pradwiyasma Widartha +4 more
TL;DR: In this paper , the authors presented text classification using genetic programming by pre-processing text using Hadoop map-reduce and collecting data using web scraping, where genetic programming is used to perform association rule mining (ARM) before text classification to analyze big data patterns.
Proceedings ArticleDOI
Feature level Fine Grained Sentiment Analysis for Classifying Online Restaurant Reviews
TL;DR: In this article , the authors conducted sentiment analysis on online restaurant reviews given by customers and classified each and every comment given in the text into five labels i.e. positive, negative, neutral, highly positive and highly negative.
References
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Book ChapterDOI
Naive (Bayes) at forty: the independence assumption in information retrieval
TL;DR: The naive Bayes classifier, currently experiencing a renaissance in machine learning, has long been a core technique in information retrieval, and some of the variations used for text retrieval and classification are reviewed.
Proceedings Article
Spam Filtering with Naive Bayes - Which Naive Bayes?
TL;DR: An experimental procedure that emulates the incremental training of person- alized spam filters is adopted, and roc curves that allow us to compare the dierent versions of nb over the entire tradeo between true positives and true negatives are plotted.
Journal ArticleDOI
Some Effective Techniques for Naive Bayes Text Classification
TL;DR: This paper proposed two empirical heuristics: per-document text normalization and feature weighting method, which performed very well in the standard benchmark collections, competing with state-of-the-art text classifiers based on a highly complex learning method such as SVM.
Book ChapterDOI
Multinomial naive bayes for text categorization revisited
TL;DR: It is shown how the performance of multinomial naive Bayes can be improved using locally weighted learning, and that support vector machines are still the method of choice if the aim is to maximize accuracy.
Journal ArticleDOI
An investigation of machine learning based prediction systems
Carolyn Mair,Gada Kadoda,Martin Lefley,Keith Phalp,Chris Schofield,Martin Shepperd,Steve Webster +6 more
TL;DR: It is shown that ANN methods have superior accuracy and that RI methods are least accurate, however, this view is somewhat counteracted by problems with explanatory value and configurability.
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