An in-depth exploration of Bangla blog post classification

Bulletin of Electrical Engineering and Informatics
Vol. 10, No. 2, April 2021, pp. 742~749
ISSN: 2302-9285, DOI: 10.11591/eei.v10i2.2873  742
Journal homepage: http://beei.org
An in-depth exploration of Bangla blog post classification
Tanvirul Islam, Ashik Iqbal Prince, Md. Mehedee Zaman Khan, Md. Ismail Jabiullah,
Md. Tarek Habib
Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
Article Info ABSTRACT
Article history:
Received Oct 11, 2020
Revised Jan 28, 2021
Accepted Feb 15, 2021
Bangla blog is increasing rapidly in the era of information, and consequently,
the blog has a diverse layout and categorization. In such an aptitude,
automated blog post classification is a comparatively more efficient solution
in order to organize Bangla blog posts in a standard way so that users can
easily find their required articles of interest. In this research, nine supervised
learning models which are support vector machine (SVM), multinomial
naïve Bayes (MNB), multi-layer perceptron (MLP), k-nearest neighbours (k-
NN), stochastic gradient descent (SGD), decision tree, perceptron, ridge
classifier and random forest are utilized and compared for classification of
Bangla blog post. Moreover, the performance on predicting blog posts
against eight categories, three feature extraction techniques are applied,
namely unigram TF-IDF (term frequency-inverse document frequency),
bigram TF-IDF, and trigram TF-IDF. The majority of the classifiers show
above 80% accuracy. Other performance evaluation metrics also show good
results while comparing the selected classifiers.
Keywords:
Bangla blog
Bangla text classification
Bigram
Supervised machine learning
TF-IDF
Trigram
Unigram This is an open access article under the CC BY-SA license.
Corresponding Author:
Tanvirul Islam
Department of Computer Science and Engineering
Daffodil International University
Mirpur Road, Dhaka, Bangladesh
Email: tanvirul15-6117@diu.edu.bd
1. INTRODUCTION
The modern world is rapidly moving towards a web-based platform for general co-operation in
content making and utilization. Many people are interested in online resources for their daily choice of
reading. Nowadays, the online blog is one of the most popular digital domains among people. Bangla text
articles on digital platforms have increased for a few years. Bangla is the most widely spoken language in
Bangladesh and the second most widely spoken in India [1]. Moreover, there was around 228 million native
Bangla speakers worldwide, 7th most communicated language on the world [1]. In such an aptitude, it needs
to organize Bangla texts in a standard way so that users can easily find their required articles of interest.
In the time of digitalization, quantities of Bangla online blogs are rising quickly and furthermore
accessible in a great amount. A lot of blog readers like to read and analyze articles from various blog sources.
Most of the time readers are interested only in the articles of their categories of interest. So, to access these
text documents easily, we need to classify the topics of these texts by using some standard automatic topic
classification methods.
Significant attention has been given by the researchers on blog post classification for languages like
English and other languages [2, 3]. Some work has been done on Bangla news classification [4, 5], emotion
detection from Bangla text [6] and malicious Bangla text detection [7]. However, there is no work done in a
Bangla blog post classification.

Bulletin of Electr Eng & Inf ISSN: 2302-9285 
An in-depth exploration of Bangla blog post classification (Tanvirul Islam)
743
Islam et al. introduced [4] a document categorization system where they used three supervised
learning model i.e. SVM, naïve Bayes (NB) and SGD. Classification of Bangla newspaper documents in
predefine twelve class was done [5] using SVM which gives promising accuracy. A new comprehensive
dataset [8] is employed to classify Bangla articles from online news portals. They used two TF-IDF and
Word2vec feature selection methods to train the five supervised learning models logistic regression, neural
network, NB, random forest and AdaBoost. SGD classifier has been used [9] to categorize the Bangla
document from BDNews24. It is observed that the proposed method performed better compared to SVM and
NB classifier. Paper [10], introduced a voting classifier (VC) to help sentiment analysis for US airline
companies based on SGD and logistic regression (LR) and used a soft voting mechanism to make the final
prediction. In [11], the authors try to detect the sentiments from Bangla microblog posts to identify the
overall polarity of texts as either negative or positive using SVM or maximum entropy (MaxEnt).
It is incorporated inverse class frequency (ICF) with term frequency (TF) and inverse document
frequency (IDF) that provide enhanced performance for feature extraction [12]. Ankita et al. [13] also used
term frequency-inverse document frequency-inverse class frequency (TF-IDF-ICF) for feature selection and
evaluate that TF-IDF-ICF has performed better compare to TF-IDF. In [14], the authors inspect the use of
cosine similarity and Euclidean distance as similarity measures on the vector space model based on TF-IDF.
The acquire accuracy for cosine similarity and Euclidean is 95.80% and 95.20% respectively. Bangla web
documents are categorized [15] using decision tree (C 4.5), k-NN, NB, and SVM while SVM was the most
successful among these four methods. Paper [16] proposed a topic modeling-based approach to extractive
automatic summarization. A general framework for short text classification by learning vector
representations of both words and hidden topics together has been presented in [17]. Almost all of the
preceding cited research deals with Bangla newspaper article classification and English blog article
classification. Our main intent in this study is to classify the Bangla blog article based on supervised learning.
2. RESEARCH METHOD
The detection of multi-lingual unorganized and frequent words is a quite difficult task. We have
employed three feature extraction technique i.e. unigram TF-IDF, bigram TF-IDF and trigram TF-IDF to
train nine supervised learning model. Before implementing classification tasks, it needs to make the dataset
accurate and valid by pre-processing which helps to train and test the data. On the pre-processing stage,
Tokenization, stop word removal, stemming and feature extraction was performed on our dataset. The Data
collection, pre-processing and feature extraction is detailed in the following subsection. After feature
extraction, the dataset is split into train and test set and train datasets are employed to train the model.
Finally, performance is evaluated to find the best classifiers and best feature extraction technique for this
classification task. Figure 1 illustrates the block diagram of the entire process.
In performance analysis, accuracy and some other metrics are required to evaluate the performance
of a classiﬁer. A confusion matrix for a binary classification has the number of true positives (TPs), false
positives (FPs), true negatives (TNs) and false negatives (FNs). For the scenario of multiclass problem, the
confusion matrix will be dimension n * n (n>2). A matrix of n rows and n columns, where is not possible to
directly calculate TPs, TNs, FPs and FNs. The value of TPs, TNs, FPs and FNs for class I is calculated as
[18]:
TPi = aii. (1)
FPi = ∑ aji
n
j=1,j≠i . (2)
FNi = ∑ aij
n
j=1,j≠i . (3)
TNi = umj=1,j≠i
n
,∑ ajk.
n
k=1,k≠i (4)
To explore the effectiveness of the model, precision, recall and F1 score are employed as evaluation
metrics which are defined as:
Accuracy =
𝑇𝑃+𝑇𝑁
𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁
(5)
Precision =
𝑇𝑃
𝑇𝑃+𝐹𝑃
(6)

 ISSN: 2302-9285
Bulletin of Electr Eng & Inf, Vol. 10, No. 2, April 2021: 742 – 749
744
Recall =
𝑇𝑃
𝑇𝑃+𝐹𝑁
(7)
F1 Score = 2 ×
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 ×𝑅𝑒𝑐𝑎𝑙𝑙
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+ 𝑅𝑒𝑐𝑎𝑙𝑙
(8)
Figure 1. Block diagram of proposed model
2.1. Description of dataset
The Dataset used in this research is created by crawling few Bangla blog as like BDnews24blog
[19], ebanglahealth [20], pavilion [21], and Vromon guide [22]. To automate the data collection process, a
web crawler is developed using python. We considered only blog posts of eight predefined domain namely,
finance, politics, technology, health, sports, entertainment, campus, and traveling. The distribution of dataset
is given Table 1.
Table 1. Distribution of data
Class Frequency
Finance 634
Politics 637
Technology 592
Health 492
Sports 406
Entertainment 638
Campus 631
Traveling 515
Total 4545

745
2.2. Data pre-processing
2.2.1. Tokenization
For better classification, it is necessary to go through tokenization and pre-processing before
applying any feature extraction technique. Tokenization is the system that intends to break the content report
into tokens delimited by a newline or tab or white spac. In this experiment, token is split based on some
delimiter like punctuation, space, newline, emoji, and special character. All the special characters,
punctuation, English and Bangla numerals, extra whitespace are eliminated in this stage. Word of other
languages except Bangla and single-character words are also removed. Now the dataset is a bag of words.
Figure 2 depicts the pseudocode of the tokenization process.
1. for i = 0 to i = length (dataset)
2. list_of_word = dataset [i] split into (r".s|?s|!s|n")
3. for j = 0 to j = length(list_of_word)
4. marged_document = marged_document+ list_of_word [j] + ” ”
5. dataset[i] = marged_document
6. end
7. marged_document = ””
8. end
Figure 2. Bangla_dataset_tokenization
2.2.2. Stop word removal
Stop words are the most repeatedly appeared words that don’t help us find relevant information of
the text documents. These words do not carry any significant information about the experiment. Some of the
words are used only for grammatical rules. So, these non-significant words create huge noise which may
damage the performance of text classification. The main reason is that they do not differentiate between
relevant and non-relevant text. Stop word are removed from our dataset. A dataset of Bangla stop word [23]
have been used for this process. The stop word dataset contains 390 stop words like “আমি”, “এবং”, “আরও”, and
“এটা”. Figure 3 represents the pseudocode of the Stopword removal process.
1. for i = 0 to i = length(dataset)
2. list_of_word = dataset [i] split into (r".s|?s|!s|n")
3. for j = 0 to j = length(list_of_word)
4. if list_of_word [j] in stopword:
5. continue
6. marged_document = marged_document + list_of_word [j] + ” ”
7. end
8. dataset [i] = marged_document
9. marged_document = ” ”
10. end
Figure 3. Bangla_stopword_removal
2.2.3. Stemming
It is essential to determine the root word of any word for an enhanced classification in case of a
highly inflectional language like Bangla. The idea is to erase inflections from a word to obtain its stem word.
There are several stemmers exist for Bangla language [24, 25]. A lightweight rule-based stemmer has been
employed [24] in this research. Sample output of the stemmer used is given in Table 2.
Table 2. Sample output of used stemmer
Input word Output word
মাসের মাে
অফিসে অফিে
থাকাটাই থাকা
এমনটা এমন
এসেই এে
চাকফরটির চাকফর

 ISSN: 2302-9285
746
2.3. Feature extraction
The purpose of feature extraction method to reduce the dimensionality of the corpus by eliminating
features that is inappropriate for the classification. Several statistical approaches can be used for feature
extraction. TF-IDF have chosen in this experiment. TF-IDF considers two scores, TF and IDF. TF count the
frequency of a term within the document, and IDF scale down the term that occurs frequently within multiple
documents, as those are less important terms. To explore the proper feature extraction method three different
string property e.g. unigram, bigram, and trigram are implemented in this experiment. Unigram features do
not take into account the relevancy of words in a sentence. Bigram features consider the relevancy of two
consecutive words within a sentence and trigram features include the relevancy between three consecutive
words.
2.4. Training
An in-depth exploration has been performed to find a proper learning model for Bangla Blog Article
classification. MNB, MLP, SVM, decision tree, random forest, SGD, ridge classifier, perceptron, and KNN
has been considered among the available learning model from the literature review. Each of the datasets is
split into an 80:20 ratio as a train set and test set. Three experiments have been conducted on this dataset.
Firstly, unigram TF-IDF, then bigram TF-IDF and trigram TF-IDF are also employed as features extraction
method with all the mentioned classifiers.
3. RESULTS AND DISCUSSION
Proposed System has been implemented on 4545 blog posts of eight distinct domains. Nine
supervised learning models i.e. MNB, MLP, SVM, decision tree, random forest, SGD, ridge classifiers,
perceptron and k-NN have been trained. Furthermore, the performance of each classifier has been
experimented using three different string property unigram, bigram and trigram. First experiment was
conducted using unigram TF-IDF features. Figure 4 illustrate the accuracy obtained by unigram TF-IDF
features.
Figure 4. Accuracy using unigram TF-IDF features
SVM achieved the highest accuracy of 0.874 followed by Ridge classifier and MLP in the case of
unigram TF-IDF features. SGD also has a decent performance. Decision tree and random forest has the
poorest performance. Secondly, another experiment was conducted using bigram TF-IDF features. The
accuracy achieved by the second experiment is in Figure 5.
Figure 5. Accuracy using bigram TF-IDF features

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In the second experiment, the accuracy of most of the classifiers decreased compares to the first
experiment (unigram TF-IDF features). The performance gap between SVM with unigram TF-IDF features
and bigram TF-IDF features are quite noticeable. In this case, Ridge classifier has the highest accuracy of
0.865. SGD is performing very close to it. Finally, the experiment has been conducted using trigram TF-IDF
features which have the following accuracy given by Figure 6.
Figure 6. Accuracy using trigram TF-IDF features
In this study, the accuracy of SGD slightly increased compares 2nd
experiment. The performance of
other models did not have any change. Like bigram TF-IDF features, hare also Ridge classifier has the
highest accuracy of 0.865. SGD is performing very close to it. The result indicates that SVM with unigram
TF-IDF features have the highest accuracy among all the experiment. Ridge classifier with unigram TD-IDF
features and SGD with bigram TF-IDF features are is second and third positions in terms of accuracy.
Decision tree, random forest and MLP with trigram TF-IDF features are in the last three positions while MLP
was performing quite better with unigram TF-IDF features. Precision, recall and F1 score have been
calculated for evaluating the performance of those models. Precision describes which portions of the
Prediction (for a given class X) were actually correct. Besides recall describes which portions (for a given
class X) was predicted correctly. F1 score is the weighted mean of precision and recall. This score takes both
into account. Precision, recall and F1 score are given in the Table 3.
Table 3. Performance of difference approaches
Feature extraction technique Learning model Precision Recall F1 Score Accuracy
Unigram TF-IDF MNB 0.83 0.79 0.78 0.79
MLP 0.86 0.86 0.86 0.86
SVM 0.88 0.87 0.88 0.87
Decision Tree 0.59 0.58 0.59 0.59
Random Forest 0.70 0.59 0.58 0.59
SGD 0.86 0.86 0.86 0.85
Ridge Classifier 0.87 0.87 0.87 0.87
Perceptron 0.83 0.83 0.83 0.84
k-NN 0.78 0.77 0.77 0.77
Bigram TF-IDF MNB 0.83 0.79 0.78 0.77
MLP 0.77 0.72 0.72 0.72
SVM 0.85 0.80 0.80 0.80
Decision Tree 0.60 0.49 0.51 0.49
Random Forest 0.70 0.59 0.58 0.42
SGD 0.86 0.86 0.86 0.85
Perceptron 0.80 0.80 0.80 0.80
k-NN 0.78 0.77 0.77 0.77
Trigram TF-IDF MNB 0.83 0.79 0.78 0.77
MLP 0.72 0.72 0.72 0.72
SVM 0.85 0.80 0.80 0.80
Decision Tree 0.60 0.49 0.51 0.49
Random Forest 0.59 0.42 0.36 0.42
SGD 0.86 0.86 0.85 0.86
Perceptron 0.80 0.80 0.80 0.80
k-NN 0.78 0.77 0.77 0.77

 ISSN: 2302-9285
748
It can be observed from Table 3 that SVM with unigram features have the highest F1 score. The
performance of SVM has dropped in the case of bigram TF-IDF features and trigram TF-IDF features. Ridge
classifier with unigram features and ridge classifier with bigram features are in the second position
considering F1 score. In terms of feature extraction, unigram has outperformed bigram and trigram. The
reason behind this is data scarcity. As N-gram length increases, the occurrence of any given N-gram
decreases. The sparser our data set, the worse we can model it. The number of events, i.e. N-grams during
training period becomes progressively less as N increases. We have a comparatively very large amount of
token types (i.e. the vocabulary of our dataset is very rich), but each of these types has a low frequency. For
this, we have got better results with a lower order N-gram model. Moreover, our training data set is not so
large, whereas a higher order of N-gram required a large number of data.
4. COMPARATIVE ANALYSIS OF RESULTS
Some relevant recent research results are compared below, to evaluate the efficiency of our
proposed approach in classifying Bangla blog article. The study of literature exposes that all of the research
reports are limited to the classification of Bangla news articles. Because of the unstructured nature of the blog
article, classification of blog articles is difficult. Besides the diversity of content of blog article make the
classification task more difficult. Furthermore, the lack of use of a common dataset of blog article makes it
difficult to have an equitable comparison of the performance of different approaches. Even though such
limitations, a review of numerical results related to Bangla blog article classification is attempted to evaluate
the comparative merits of this work. The overview of all the approaches including our approach is given in
Table 4.
Table 4. Comparison with our proposed works
Work done No of
classes
Sample
size
Source of dataset Best performed feature
selection technique
Best performed
classifier
Accuracy
Proposed Work 8 4545 Bangla Blog Unigram TF-IDF SVM 87.4%
Dhar et al. [13] 8 4000 Online New Portal TF-IDF-ICF MNB 98.87%
Dhar et al. [12] 8 4000 Online New Portal TF-IDF-ICF MLP 97.65%
Mandal et al. [15] 5 1000 Online New Portal TF-IDF SVM 89.14%
Alam et al. [8] 5 376226 Online New Portal TF-IDF Neural Network 96%
Dhar et al. [14] 5 1000 Online New Portal TF-IDF Cosine Similarity 95.8%
Islam et al. [4] 12 31,000 Online New Portal TF-IDE SVM NA
1
NA: Not Mentioned
In the work [13], they classify news documents with a dataset of size 4000. MNB is giving
promising result is there experiment. In Bangla web document classification [12] process, text documents of
online news articles are classified where MLP achieved the highest accuracy. For developing a Bangla web
document categorization system [15], different classifier has experimented where SVM with TF-IDF feature
selection technique performed the best. News article classification using a comprehensive dataset [8] is done
where neural network with TF-IDF feature selection technique has the most decent performance. Paper [14]
introduced a text document classification approach where cosine similarity with TF-IDF feature selection
technique achieved an accuracy of 95.8%.
Considering the discussed scenario and challenges in classification of blog articles, our obtained
accuracy is more than 87% which is quite good. As we have specified earlier, the absence of the same
dataset, dataset collected from a different domain and different source (Bangla blog instead of news portal) it
is not equitable to explicit comparison of our work with others. Besides, it may not be improper to claim that
our proposed approaches have enough distinguishing information to classify Bangla blog post.
5. CONCLUSION AND FUTURE SCOPE
The domain of the Bangla blog is rapidly expanding, so automated classification can be exceedingly
beneficial. In this system, a dataset of 4545 instances from 8 distinct domains has been collected from
various Bangla blogs. We have experimented with different learning models and different features selection
approaches. After comparing performance, it is observed that SVM with unigram TF-IDF features achieved
the highest accuracy and F1 score of 0.87 and 0.88 respectively. SGD and ridge classifiers also perform
closely to SVM. SGD and ridge classifier with unigram features obtained an accuracy of 0.85 and 0.86.
Another observation is, for most of the cases unigram is performing better compare to bigram and trigram.

749
We expect that our dataset will help the Bangla NLP researcher to extend this Bangla blog article
classification task. Besides this corpus can be employed in other Bangla NLP tasks.
ACKNOWLEDGEMENTS
The authors have received no financial support for the research, authorship, and/or publication of
this article.
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