Naive Bayes classifier for continuous variables using novel method (NBC4D) and distributions

Yıldırım, Pelin; Birant, Derya

doi:10.1109/inista.2014.6873605

Cited by 19 publications

(10 citation statements)

References 8 publications

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“…The NB classifier is a well-known statistical supervised learning algorithm based on Bayes’ Theorem [30]. Conditional probabilities are calculated using all training sets to determine the category in which the text document should be classified.…”

Section: Materials and Methods Appliedmentioning

confidence: 99%

TTC-3600: A new benchmark dataset for Turkish text categorization

Kılınç

Özçift

Bozyiğit

et al. 2015

Journal of Information Science

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Owing to the rapid growth of the World Wide Web, the number of documents that can be accessed via the Internet explosively increases with each passing day. Considering news portals in particular, sometimes documents related to categories such as technology, sports and politics seem to be in the wrong category or documents are located in a generic category called others. At this point, text categorization (TC), which is generally addressed as a supervised learning task is needed. Although there are substantial number of studies conducted on TC in other languages, the number of studies conducted in Turkish is very limited owing to the lack of accessibility and usability of datasets created. In this paper, a new dataset named TTC-3600, which can be widely used in studies of TC of Turkish news and articles, is created. TTC-3600 is a well-documented dataset and its file formats are compatible with well-known text mining tools. Five widely used classifiers within the field of TC and two feature selection methods are evaluated on TTC-3600. The experimental results indicate that the best accuracy criterion value 91.03% is obtained with the combination of Random Forest classifier and attribute ranking-based feature selection method in all comparisons performed after pre-processing and feature selection steps. The publicly available TTC-3600 dataset and the experimental results of this study can be utilized in comparative experiments by other researchers.

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Section: Materials and Methods Appliedmentioning

confidence: 99%

TTC-3600: A new benchmark dataset for Turkish text categorization

Kılınç

Özçift

Bozyiğit

et al. 2015

Journal of Information Science

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“…Spark MLlib NB may be trained within a single pass through data set and, therefore, it does not need to cache training data. Spark MLlib NB model implements a multinomial Bayesian approach, which is particularly efficient for text analysis tasks . The NB takes RDD of class labels and related features and produces a model for the prediction/evaluation purposes.…”

Section: Use Case: Generic Sentiment Analysis Frameworkmentioning

confidence: 99%

A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data

Kılınç

2019

Softw Pract Exp

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Summary There are many data sources that produce large volumes of data. The Big Data nature requires new distributed processing approaches to extract the valuable information. Real‐time sentiment analysis is one of the most demanding research areas that requires powerful Big Data analytics tools such as Spark. Prior literature survey work has shown that, though there are many conventional sentiment analysis researches, there are only few works realizing sentiment analysis in real time. One major point that affects the quality of real‐time sentiment analysis is the confidence of the generated data. In more clear terms, it is a valuable research question to determine whether the owner that generates sentiment is genuine or not. Since data generated by fake personalities may decrease accuracy of the outcome, a smart/intelligent service that can identify the source of data is one of the key points in the analysis. In this context, we include a fake account detection service to the proposed framework. Both sentiment analysis and fake account detection systems are trained and tested using Naïve Bayes model from Apache Spark's machine learning library. The developed system consists of four integrated software components, ie, (i) machine learning and streaming service for sentiment prediction, (ii) a Twitter streaming service to retrieve tweets, (iii) a Twitter fake account detection service to assess the owner of the retrieved tweet, and (iv) a real‐time reporting and dashboard component to visualize the results of sentiment analysis. The sentiment classification performances of the system for offline and real‐time modes are 86.77% and 80.93%, respectively.

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“…Naive Bayes is a supervised learning algorithm in the machine learning [8]. Specially, NB is being used for classification.…”

Section: Naive Bayes Classifiermentioning

confidence: 99%

Naive Bayes Classifier Based Geographic Information System for University Search Information

Zeniarja

Luthfiarta

Supriyanto

2018

JAIS

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Information about the geographical location of universities is necessary for graduates of Senior High School who want to continue their education to a university. Most of the graduate students do not know the location of the universities since the geographical location of Google Maps is less clear and less precise. Therefore, the application of Geographic Information Systems (GIS) based on Information Retrieval (IR) is expected to facilitate the graduate students to know the exact location of the university. In this paper, IR-based GIS application is developed by using web programming. The web is used as a search engine when someone wants to find a college. The application shows the map and information of the college in the area according to the query of the user. Naive Bayes algorithm is used to classify the user query and locate the query on the map. Based on our prototype, the application is promising to be implemented for the student.

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Naive Bayes classifier for continuous variables using novel method (NBC4D) and distributions

Cited by 19 publications

References 8 publications

TTC-3600: A new benchmark dataset for Turkish text categorization

TTC-3600: A new benchmark dataset for Turkish text categorization

A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data

Naive Bayes Classifier Based Geographic Information System for University Search Information

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