This document summarizes a research paper that compared different machine learning algorithms for automated news categorization. The researchers used a news article dataset from Kaggle to test Naive Bayes, Support Vector Machine (SVM), and Neural Network classifiers. SVM performed best with an accuracy of 75.84%, execution time of 243 milliseconds, and mean absolute error of 0.28. The paper concludes SVM is the best algorithm for classifying news articles out of the three compared based on accuracy, speed and error rate.

1. Jour of Adv Research in Dynamical & Control Systems, Vol. 11, 07-Special Issue, 2019
ISSN 1943-023X 1423
Received: 16 July 2019/Accepted: 05 August 2019
Automated News Categorization Using
Machine Learning Techniques
V. Vijeya Kaveri, Associate Professor, Department of IT, Sathyabama Institute of Science and Technology.
J. Jabez, Associate Professor, Department of IT, Sathyabama Institute of Science and Technology.
P. Jeyanthi, Associate Professor, Department of IT, Sathyabama Institute of Science and Technology.
Abstract--- As time goes on, digitization of text has been increasing day by day and there is a need to classify
them. Text classification has become very essential in this situation. Text analysis is used for information extraction,
information retrieval and pattern recognition. The problem of text analysis is, it takes much amount of time to
complete work as various methods and various algorithms are being used in this process. Each method being used in
the classification has different accuracy rate. The idea of the project is to compare different machine learning
algorithms and to find the best possible solution for the highest accuracy. To evaluate the model efficacy, News
Articles dataset from kaggle has been used. Then, the input can be given as different News Articles and these News
Articles are filtered into different categories according to their genre i.e., sports news, business news, health news,
world news etc. The machine learning algorithms can be Naive Bayes’ classifier, Support Vector Machine and Neural
Networks which are being compared to get the accurate result. The above comparison can be represented using
graphs. This will help in searching the News, category wise.
Index Terms--- News Articles, Classification, Naive Bayes’ Classifier, Support Vector Machine, Neural Networks.
I. Introduction
In text classification, various statistical and machine learning techniques are carried out in order to automatically
assign one among the predefined labels to a given detail of the unlabeled document. For example if D is a set of
documents and d is a document within the entire set and C = {c1, c2, c3, …,cn } is the set of all of the categories, in the
text classification process one of the category ci will be assigned to the given document d.
There is a huge amount of data growing nowadays on the internet which is in the form of verity of news from
different sources of the world. In everyday scenarios, the ability to automatically classify documents into a fixed set of
categories is highly desirable; newspaper articles can be classified as 'features', 'sports' or 'news'. Other scenarios
involve classifying of documents as they are created. Natural language processing offers powerful techniques for
automatically classifying documents. These techniques are predicated on the hypothesis that documents in different
categories distinguish themselves by features of the natural language contained in each document. Salient features for
document classification may include word structure, word frequency, and natural language structure in each
document.
Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern
recognition, annotation, information extraction, data mining techniques including the link and association analysis,
visualization, and predictive analytics.
The goal is, essentially, to turn text into data for analysis, via the application of natural language processing (NLP)
and analytical.
II. Literature Review
In this research, Vignesh Rao and Jayant Sachdev implemented machine learning techniques to classify news
articles belonging to a particular location. The location can be a city, state, country, etc. The news articles from various
websites like, Indian Express, Hindustan Times, Times of India, etc. are extracted to form their dataset. The
underlying structure of the Web Page is the HTML language.[1]
In 2018, there was an article published by M. Thangaraj and M. Sivakami on Text classification techniques. This
article is a literature review of various studies related to text classification approaches. Statistical topic modeling is
applied for multi-label document classification, where each document gets assigned to one or more classes.

2. Jour of Adv Research in Dynamical & Control Systems, Vol. 11, 07-Special Issue, 2019
ISSN 1943-023X 1424
Received: 16 July 2019/Accepted: 05 August 2019
It has been found more important to study and understand the nature of data before proceeding into mining. The
automation of text classification process is required, with the increasing amount of data and need for accuracy.[2]
In 2016, Victoria Bobicev and Moldova worked with the data collected from the forums and manually annotated
using several labels. The data and annotation were described. They obtained the data where each forum post was
considered as an annotation unit and annotated with up to three labels. Thus, the aim was to detect all of these labels
for every analyzed post.[3]
Text Classification (Spam) Using Machine Learning was done by Neetu Sharma, Gaganpreet Kaur and Ashish
Verma in the year 2014. The vector model for representation of texts has been offered here. In the elementary case, the
model assumes comparison to each document of a frequency spectrum of words. The dimension of space is reduced by
rejection of the most common words that increases thereby percent of the importance of the basic words in more
advanced models.[4]
The field of text analysis consists of many different sub-fields, that all require different approaches. There are
some problems in text and literature analysis systems that are universal to all approaches. The problem of text analysis
is, it takes much amount of time to complete a work as various methods and various algorithms are using for this
process. The methods and the analysis systems using for this process are very costly.
III.Different Machine Learning Algorithms
This section discuss about the different machine learning algorithms that are used .
A. Naive Bayes Classifier
Naive Bayes Classifier is a classification technique based on Bayes’ Theorem. It is a real time predictor and
learning classier which is used for making predictions in real time. A Naive Bayes classifier assumes that the presence
of a particular feature in a class is unrelated to the presence of any other feature. Naive Bayes model is easy to build
and particularly useful for very large data sets. We can predict the probability of multiple classes to target variable.
Naive Bayes classifier uses Machine learning and data mining techniques to filter unseen information and predict the
data.
B. Neural Networks
A neural network is a network of neurons which is made up of real biological neurons. It is a mathematical model
which is designed to behave like nervous system. Neural networks work in very similar manner. The model is used to
recognize complex patterns and relationships that exists within a labeled data. It takes several inputs and start
processing through multiple neurons from multiple hidden layers and returns the result using an output layer. This
result analysis process is known as Forward Propagation.
C. SVM Classifier
Support Vector Machine (SVM) is a supervised machine learning algorithm which can be used for both
classification and regression challenges. It is mostly used in classification problems. SVM are co-ordinates of
individual observation.
SVM is a frontier and the model extracts a best possible line (hyper-plane) that segregates the two classes. If
outlier occurs, SVM has a feature to ignore outliers. So, we can find the hyper-plane or line that maximize to classes.
We cannot have linear hyper-plane between two classes.
IV.Experimentation and Results
The goal of this paper is to find the best among the various system mastering algorithms mentioned in previous
segment for classifying news articles. With the help of WEKA 3.8[8] tool we have built different classifiers for these
algorithms.
These algorithms are directly applied to the dataset. WEKA tool provides the built in support for data
pre-processing, classification, regression, clustering, association rules, and visualization. New machine learning
schemes are also supported in WEKA. We have tested our dataset by applying 5-cross fold validation in order to
check the outcomes of each classifier for unknown instances. The effects of entire experimentation are proven in
Table 1. While Figure 1, suggests the pictorial illustration of efficiency of those algorithms for our dataset.

3. Jour of Adv Research in Dynamical & Control Systems, Vol. 11, 07-Special Issue, 2019
ISSN 1943-023X 1425
Received: 16 July 2019/Accepted: 05 August 2019
Table 1: Comparison of Different Machine Learning Algorithms
Algorithm Efficiency (%) Time Taken (Milliseconds) Mean Absolute Error
Naive Bayes 67.35 175 0.34
Neural Networks 71.67 327 0.37
Support Vector Machine 75.84 243 0.28
Figure 1: Efficiency of different Machine learning Algorithms
Figure 2: Execution Time of different Machine learning Algorithms
Figure 3: Mean Absolute Error of different Machine learning Algorithms
Figure1, Figure 2 & Figure 3 shows that Support Vector Machine algorithm performs better in terms of efficiency,
execution time and MAE compared to other two algorithms.

4. Jour of Adv Research in Dynamical & Control Systems, Vol. 11, 07-Special Issue, 2019
ISSN 1943-023X 1426
Received: 16 July 2019/Accepted: 05 August 2019
V. Conclusion
In this paper, we have compared the efficiency, execution time and MAE of 3 Machine Learning algorithms Navie
Bayes, SVM and Neural network for classifying news articles.
VI.Acknowledgment
This work has been carried out at DST-FIST sponsored Wireless Sensor Network and IOT Lab (order Sanction
No.: SR/FST/ETI-413/2018Dated: 08th February, 2018), Department of Computer Science and Engineering,
Sathyabama Institute of Science and Technology.
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