A HYBRID CLASSIFICATION ALGORITHM TO CLASSIFY ENGINEERING STUDENTS’ PROBLEMS AND PERKS

International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.6, No.2, March 2016
DOI : 10.5121/ijdkp.2016.6206 73
A HYBRID CLASSIFICATION ALGORITHM TO
CLASSIFY ENGINEERING STUDENTS’
PROBLEMS AND PERKS
Mitali Desai and Mayuri A. Mehta
Department of Computer Engineering,
Sarvajanik College of Engineering and Technology, Surat, India
ABSTRACT
The social networking sites have brought a new horizon for expressing views and opinions of individuals.
Moreover, they provide medium to students to share their sentiments including struggles and joy during the
learning process. Such informal information has a great venue for decision making. The large and growing
scale of information needs automatic classification techniques. Sentiment analysis is one of the automated
techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to
classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful
to draw decisions in education system since they classify the sentiments into merely three pre-set
categories: positive, negative and neutral. Moreover, classifying the students’ sentiments into positive or
negative category does not provide deeper insight into their problems and perks. In this paper, we propose
a novel Hybrid Classification Algorithm to classify engineering students’ sentiments. Unlike traditional
predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process
descriptive. Moreover, it classifies engineering students’ perks in addition to problems into several
categories to help future students and education system in decision making.
KEYWORDS
Sentiment analysis, opinion mining, education system, Twitter
1. INTRODUCTION
Social media has emerged as a highly useful personal communication technology which provides
a large platform for individuals to share their feeling, emotions, and opinions in an informal and
casual way to seek social support. The opinion data available on internet covers all the recent
trends, issues, opinions on every thinkable topic [1-2]. The students’ informal talks on social
media platforms such as Twitter, Facebook, and YouTube shed insight into their present or past
learning experiences. Mining such homogeneous data extracts useful pattern from the large
volume of students’ generated raw data to support decision making in education system.
The education data mining involves two broad categories of research: front stage study and back
stage study [3]. Front stage study is usually conducted in the controlled class room environment
by making student fill the questionnaires or feedback forms. The defined format of answers, the
guarded ambience and requirement of spontaneous answers are the shortcomings of this method

74
[1-4]. Moreover, the collected responses have structured format that makes the outcome of the
study predictable. Additionally, front stage study techniques are very time consuming, especially
when a number of students is large and hence, the analysis process cannot be carried out
frequently [3-4]. Moreover, they provide limited scalability [5]. On the other hand, the back stage
study provides relaxed environment allowing students to share their honest opinions on the social
media platform. Such data provides concrete and essential knowledge about students’ emotions
toward the learning experiences.
Social media serves as a substantial platform to provide large input data for research. Therefore,
the majority of the analytics use social media platforms for data collection. However, the
abundance of data, variety of platforms, use of slangs, timing of different posts on web, and
diversity of language make the analysis process difficult to gain the hidden emotions in
educational decision making [3-4]. Hence, an efficient analysis and classification technique is
required that incorporates qualitative analysis and automated technique to get deeper insight into
the students’ sentiments.
The existing classification techniques are useful in disaster relief and humanitarian assistance,
marketing predictions, checking customer loyalty, finding job opportunities, population health
care and understanding students’ learning experiences [1-7]. The recommendation systems,
websites that disclose job opportunities, push notification and Google health care applications are
some of the examples. The educational system is still lagging behind in the field of descriptive
sentiment analysis. To the best of our knowledge, very few attempts have been made to mine and
classify students’ data generated on uncontrolled social media space to get deeper understanding
of their problems along with their perks.
In this paper, we propose a novel Hybrid Classification Algorithm (HCA) for descriptive
sentiment analysis to understand students’ problems and perks deeply. We integrate both
subjective analysis and data mining techniques to make the process descriptive. The proposed
algorithm classifies engineering students’ sentiments into several categories that help future
students and education system in decision making. We have developed a dynamic method to
generate various categories. The dynamic process eliminates the requirement of changing the
algorithm for newly added data. In our study, we have collected data from Twitter that has
witnessed a tremendous growth in the number of users recently [8-9]. The Tweets of engineering
students on Twitter consist of #engineeringProblems and #engineeringPerks hashtags. Tweets are
mostly public and limited to 140 characters that simplify identification of emotions in text [9-11].
The rest of the paper is organized as follows: In section 2, we discuss the existing work related to
sentiment analysis. Section 3 describes the proposed approach. Finally, Section 4 specifies the
conclusion and future directions.
2. RELATED WORK
In recent years, a voluminous amount of research has been done in the field of sentiment analysis.
In [12], authors have presented the logical approach for extraction of the sentiment on widely
used social networking sites. They have analysed the sentiments of the text using combinatory
categorical grammar, lexicon acquisition and annotation, and semantic networks for analysing.
The basic techniques of sentiment classification and the methods for data collection are presented
in [13]. Classification accuracy of the feature vector is tested for electronic products domain

75
using different classifiers such as Nave Bayes, SVM, Maximum Entropy, and Ensemble
classifiers in [14]. In [15], authors have introduced a hybrid method that combines usage of
sentiment lexicons along with a machine learning classifier for polarity detection of opinionated
texts in the domain of consumer-products. In [16], authors have proposed a set of techniques of
machine learning with semantic analysis for classifying the sentence and product reviews based
on twitter data using WordNet for better accuracy. In [17], authors have examined the
performance of different classifiers such as Naive Bayesian, SMO, SVM and Random Forest to
classify Twitter data.
From our in-depth study on the existing techniques, it is observed that the existing techniques
typically classify the text data into pre-set categories: positive, negative and neutral. Classifying
the students’ sentiments into positive or negative category does not provide deeper insight to their
problems. In [7], authors have proposed a technique to classify students’ data generated on
Twitter into various categories. However, the category generation process is static. Moreover, the
static process provides limited accuracy of the classifier. Our proposed algorithm provides the
dynamic method to generate various categories. The dynamic process eliminates the requirement
of changing the algorithm for newly added data. Moreover, it increases the accuracy of the
classifier. Additionally, the proposed algorithm identifies students’ perks in addition to problems.
3. THE PROPOSE HYBRID CLASSIFICATION ALGORITHM
The sentiment analysis of student data is an emerging field that needs much more attention. In
addition, classifying students’ emotions into just positive or negative opinions do not shed any
light into the actual problems in the existing learning process. The proposed hybrid classification
algorithm classifies students’ problems and perks shared on Twitter into various belonging
categories rather than merely positive or negative. Thus, the proposed algorithm makes the
sentiment analysis process descriptive. The descriptive technique is more useful to get deeper
insight into students’ problems and perks than the traditional predictive techniques. Figure 1
shows the processing steps of hybrid classification algorithm.
Figure 1. Processing steps of hybrid classification algorithm

76
3.1 Data Source
Selection of data source to conduct the back stage study to classify students’ problems and perks
plays significant role. Social media platform as the data source are categorize into three general
categories: blogs, micro-blogging sites, and review site [12-15]. Among all categories, a micro-
blogging site such as Twitter has gained higher popularity due to its limited strength of the
content. Hence, in our study, we have collected students’ data from Twitter.
The message posted on Twitter is called Tweet. Tweets are limited to 140 characters. They are
composed of any of the followings [16] [18-19]: text, links, images, and six seconds video. The
mining process is applied to classify these components into positive or negative categories. The
Tweets contain three notations which are hashtags (#), retweets (RT), and account Id (@).
There are three possible ways to collect Twitter data for research as follows.
• Data repositories such as UCI, Friendster, Kdnuggets, and SNAP
• Application Program Interfaces (APIs): Twitter provides two types of APIs such as
search API and stream API
• Automated tools that are further classified into premium tools such as Radian6 [18],
Sysmos, Simplify360, Lithium, SproutSocial and non-premium tools such as Keyhole,
Topsy, Tagboard and SocialMention
Amongst above mentioned tools, Topsy is a Graphical User Interface (GUI) based non premium
tool that makes data extraction process non-tedious. Hence, we have collected Twitter data of
engineering students consisting of #engineeringProblems and #engineeringPerks hashtags using
automated tool Topsy.
3.2 Data Pre-processing
The collected data is raw data. In order to apply classifier it is necessary to pre-process the raw
data. The pre-processing task involves uniform casing, removal of hashtags and other Twitter
notations (@, RT), stop words, and decompression of slang words.
3.3 Generation of Categories
From the qualitative analysis of the collected data, we generate different categories to classify
students’ problems and perks. In order to make the category generation process dynamic, we
construct the corpus. The corpus consists of all the possible words from the dataset which
encompasses a single class category.
3.4. Sentiment Classification Techniques
There are typically two techniques to identify sentiment of the text [7] [12-13] [18-19]:
knowledge based method and machine learning methods. To make our classification algorithm
hybrid, we combine both classification techniques. Hybrid approach increases the accuracy of
classifier and makes the category generation process dynamic. Firstly, we create a corpus to

77
make the category generation process dynamic using knowledge based method. The dynamic
category generation process eliminates the requirement of changing the algorithm for newly
added data. Secondly, we apply machine learning method to classify the input data into several
problems and perks categories.
Knowledge based method is also called Lexicon based method. The lexicon-based approach
depends on finding the opinion lexicons that are used to analyse the text. This method requires
preparation of dictionary or corpus containing all the possible opinion words with their polarities
i.e. positive or negative. It finds opinion seed words and subsequently, it searches the dictionary
of their synonyms and antonyms. A small set of opinion words is collected manually with known
orientations. The set is grown by searching in the well-known lexicon dictionary tool such as
WordNet or Sentiful for their synonyms and antonyms [16-17].
Machine Learning methods make machine to learn from the past experiences and to behave like a
human. They are further classified into supervised and unsupervised methods [12] [14-24]. To
carry out sentiment analysis, typically the supervised machine learning methods are used.
Supervised learning methods highly depend on training set and test set. Training set is already
labelled data whereas test set is used to validate performance of the built classifier. A large
number of supervised machine learning algorithms such as Naïve Bayes, Logistic Regression,
Support Vector Machine, Maximum Entropy, Decision Tree, Random Forest, and Bayesian
Network are used for sentiment analysis [7] [12-22]. Choice of an appropriate algorithm for
selected domain is a crucial step. It has been observed that the following machine learning
algorithms are widely used and give average accuracy in majority of domains as well as with
different types of data. Moreover, they provide consistent speed of classification process
irrespective of the size of input data while handling the outliers
1) Naïve Bayes (NB) Approach
Naïve-Bayes classifier [7] [17-20] is a simple probabilistic classifier which uses Bayes Theorem
and finds maximum likelihood probability of a word belonging to a particular class. The
probability P is defined as follows:
Naïve -Bayes classifier suffers from an assumption that all the features in the feature space are
independent. The frequency counts of the words are stored in hash tables during the training
phase. As per the definition of probability, the document d is classified into class c using
following equation:
2) Maximum Entropy
The maximum entropy technique is a probability distribution estimation technique. It is used for
various natural language processing tasks such as text classification. It depends on the
probabilistic approach like Naïve Bayes [21-22]. The underlying principle of maximum entropy
is that if more information regarding the data is not known, the distribution should be as uniform

78
as possible. This constraint allows the distribution to be minimally non uniform. The probability
is derived from the labelled training data and is represented as expected values of features as
follows:
Where fi(c,d) is a feature, ƛi is a parameter to be estimated and Z(d) is a normalization function.
Unlike NB, maximum entropy doesn’t make any assumption regarding the feature dependency.
The motivating idea behind maximum entropy is that one should prefer the most uniform models
that satisfy any given constraints.
3) Support Vector Machine (SVM)
The main principle of SVMs is to determine a linear separator that separates the different classes
in the search space with maximum distance [12-16]. If we symbolize the hyper plane as h, the
tweet as t, and represents the classes as Cj € {l, -1} into which the tweet has to be classified, the
solution can be written as follows corresponding to the sentiment of the tweet.
In our Hybrid Classification Algorithm (HCA), we apply SVM to classify the input data into
several categories since SVM provides higher theoretical accuracy in terms of correctly
classifying the input data into valid categories. Moreover, it provides better speed of
classification process compare to other algorithms. Additionally, SVM supports the maximum
margin technique by separating two data points with the maximum possible distance to avoid
overlapping. Figure 2 shows the hybrid classification algorithm.
Figure 2. The proposed hybrid classification algorithm
Input: a dataset T = {t1, t2, t3, ……., tn} of n Tweets that consist of #engineeringProblems
and #engineeringPerks hashtags
Output: classification of n Tweets into m categories c1, c2, c3, …., cm
Hybrid Classification Algorithm (T, n)
BEGIN
1. for i = 1 to n Tweets
2. Convert ti into uniform case
3. Remove Twitter notations, emoticons, URLs, and stop words from ti
4. Compress the elongated words in ti
5. Decompress the slang word in ti
6. Generate c1, c2, …, cm dynamically into corpus using lexicon method
7. Apply SVM to classify n Tweets into their respective belonging class categories
END

79
In HCA, first n Tweets of Engineering students consisting of #engineeringProblems and
#engineeringPerks hashtags are collected. Subsequently, the collected data is pre-processed
applying steps 2-5. In pre-processing, first the Tweets are converted in uniform casing i.e. either
upper case or lower case. Then the Twitter notations such as hashtags (#), retweets (RT), and
account Id (@) are removed. Moreover, the URLs and emoticon are also removed. It is
necessary to remove non letter data and symbols as we are dealing with only text data. In
addition, stop words (i.e. are, is, am) are removed. The stop words do not emphasize on any
emotions, it is intended to remove them to compress the dataset. Secondly, compress the
elongated words such as happyyy into happy. Thirdly, the slag words such as g8, f9 are
decompressed. Generally slang words are adjectives or nouns and they contain the extreme level
of sentiments. So it is necessary to decompress them. In step 6, we generate the corpus including
all possible word belonging to different identified categories. We make the category generation
process dynamic by constructing the corpus using lexicon method. In step 7, we apply the
supervised machine learning classifier called SVM to classify the n Tweets into their belonging
categories. SVM provides higher theoretical accuracy in terms of correctly classifying the input
data point into a valid category. Moreover, it provides better speed of classification process
compare to other algorithms.
The generated categories shed insight into students’ actual existing problems and perks deeply
than merely identifying positive or negative emotions. Moreover, they guide future students in
determining their educational field. Furthermore, they are useful in improving the quality of
education system.
4. CONCLUSION AND FUTURE WORK
In this paper, we have proposed a novel Hybrid Classification Algorithm that makes the
traditional predictive sentiment analysis process descriptive. To make sentiment analysis process
descriptive, we have incorporated qualitative analysis along with data mining techniques. The
descriptive process classifies engineering students’ Twitter data into their belonging problems
and perks categories rather than just positive or negative content identification. The descriptive
categories guide the future students in their selection of educational stream. Moreover, based on
the descriptive categories, the quality of education system can be improved further. In addition,
the proposed algorithm makes the category generation process dynamic that eliminates the need
to change the algorithm each time when new data is added. The dynamic category generation
processes increase the accuracy of the classifier. In future, we aim to evaluate the performance of
the HCA under different data collection methods and data set of different sizes.
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AUTHORS
Mitali Desai received the B.E. degree in Information Technology from Sarvajanik
College of Engineering and Technology, Gujarat Technological University, Surat,
India in 2012. She is pursuing M.E. in Computer Engineering at Sarvajanik
College of Engineering and Technology, Gujarat Technological University, Surat,
India. She has worked as a Teaching Assistant in the Sardar Vallabhbhai National
Institute of Technology (SVNIT), Surat, India for 2 years from 2012 to 2014. She
has also worked as an Ad-hoc lecture in Sarvajanik College of Engineering and
Technology, Surat, India. Her research areas are Data Mining, Web Mining, Soft
Computing, BigData Analytics and Artificial Intelligence.
Mayuri A. Mehta received the B.E. and the M.E. degrees in Computer
Engineering from Sardar Patel University, Vallabh Vidhyanagar, India, in 2000
and 2005, respectively and the Ph.D. degree in Computer Engineering from the
Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India, in
2014. She started her career as Lecturer in 2000 and presently she is working as
Associate Professor with Department of Computer Engineering, Sarvajanik
College of Engineering and Technology, Surat, India. Her current research
interests include Distributed Computing Paradigms, Analysis and Design of
Algorithms and BigData Analytics.

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