A Framework for Aspect level Sentiment Analysis of ... · framework was implemented using the...

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A Framework for Aspect level Sentiment Analysis of Academic Results

Data

A. Jenifer Jothi Mary1, L. Arockiam

2

1Research Scholar, Department of Computer Science, St. Joseph’s College (Autonomous), Tiruchirapalli,

India. 2Associate Professor, Department of Computer Science, St. Joseph’s College (Autonomous), Tiruchirapalli,

India.

Abstract – The rapid growth of internet users in most of the websites, blogs and forums allow the

users to write their opinions as reviews for various products or services. Sentiment analysis tries to

determine the sentiment of a writer about some aspects and also the overall contextual polarity of a

document. Sentiment Analysis (SA) or Opinion Mining (OM) is a process for tracking the mood of

the people about any particular topic. Aspect based or Aspect level opinion mining is proposed to

perform aspect extraction and sentiment classification using the features mentioned in the reviews.

Many educational organizations use stakeholders review as important feedback for the development

of an organization. The aim of this paper is to analyze the students’ text comments using Aspect

based sentiment analysis of the semester exam results. The objective is to analyze the sentiments

expressed in textual form by staff and students.

Key words – Aspect; Aspect extraction; sentiment classification; polarity; opinion Mining

I. INTRODUCTION

Increasing popularity and availability of internet online review sites, blogs, and social networking

sites increase the contents rapidly day by day. Sentiment analysis is an emerging research area and it

can be done at three levels: document level, sentence level and feature level [See, 2015]. Document

level analysis provides information about each document’s polarity. The polarity is represented as

either positive or negative. Sentence level analysis provides polarity of each sentence’s in a

document. Aspects are components or attributes or properties of a product or services on which

opinions are expressed. The researchers are also use aspect as feature, topic or opinion target. Aspect

/ Feature level analysis gives polarity of particular feature in a document [Bin, 2012] [Aks, 2012].

The major tasks of feature based opinion mining are feature selection / extraction, sentiment

classification, polarity measurement and summarization.

The college management system using Big Data includes several modules namely Administration,

Staff management system and student management system. Fig. 1 shows the basic system of college

management and provides wide range of modules. These modules are designed to provide specific

functionalities in the context of college management system.

International Journal of Recent Trends in Engineering & Research (IJRTER)

@IJRTER-2016, All Rights Reserved

Jose L. Hurtado et al. [Jos, 2016] proposed topic discovery and forecasting framework. It use

association analysis to identify a set of topics followed by a t

analysis discovered correlations between topics and identified

communities. Then, an ensemble forecasting approach

research topics. The data used for experimental study

in ACM-KDD, IEEE-ICDM, SIAM

paper from 2002–2010, which result

proposed combined prediction framework

values, MSE and the standard deviation of the error, than baseline methods.

Kalpana Razdan et al. [Kal, 2015] proposed methodology diagram to evaluate document level

sentiment analysis by obtaining aspect level sentiment score and its weightages. The aspect based

document level sentiment analysis accuracy

document level. It presented a multi

sentiments.

N. D. Valakunde et al. [Val, 2013]

from the distinct aspect based

accreditation criteria such as, knowledge, presentation, communication and regularity of the faculty.

The importance of these aspects towards computation of document level score

the weightages. This strategy prov

scores computed. It also showed that SVM has better accuracy over NB.

Htay et al. [Hta, 2013] proposed to get the patterns of opinion words/phrases about the feature of

product from the review text through adjective, adverb, verb, and noun. The e

opinions were useful for generating a meaningful summary that provide significant informative

resource and expected to achieve good results.

Khan et al. [Khan, 2015] discussed various

Aspect-based Sentiment Analysis. The extended topic models used for Aspect

analysis were supervised, unsupervised, semi

Knowledge-based topic models. The Knowledge based models appreciated because it d

training or domain experts which

which are frequently available of social media and other review websites.

Yanyan Zhao et al. [Yan, 2015] proposed an aspect

issue. It had a two-step framework for the sentiment analy

alignment classifier that has basic, relational, and special target

Controller of

Examination

•


Volume 02, Issue 07; July

2016, All Rights Reserved

Fig. 1 College Management System

II. RELATED WORKS

Jose L. Hurtado et al. [Jos, 2016] proposed topic discovery and forecasting framework. It use

association analysis to identify a set of topics followed by a temporal correlation analysis

ons between topics and identified a network of topics

ensemble forecasting approach was proposed to predict the popularity of

research topics. The data used for experimental study was collected from scientific papers published

ICDM, SIAM-SDM, and ICML. They used both the abstract and title of each

2010, which resulted in 6122 papers as the dataset. Experiments confirmed the

framework yields better performance, with respect to the R

values, MSE and the standard deviation of the error, than baseline methods.

Razdan et al. [Kal, 2015] proposed methodology diagram to evaluate document level

btaining aspect level sentiment score and its weightages. The aspect based

document level sentiment analysis accuracy was high, compared to sentiment analysis at

a multi-aspect sentiment analysis providing close

N. D. Valakunde et al. [Val, 2013] suggested to compute document level faculty performance score

from the distinct aspect based sentiment scores. The aspects were taken from the NAAC


The importance of these aspects towards computation of document level score

the weightages. This strategy provided more accurate document level sentiment scores than the

that SVM has better accuracy over NB.


t through adjective, adverb, verb, and noun. The e

re useful for generating a meaningful summary that provide significant informative

resource and expected to achieve good results.

Khan et al. [Khan, 2015] discussed various extensions of topic models that focus on problems of

based Sentiment Analysis. The extended topic models used for Aspect

supervised, unsupervised, semi-supervised, hybrid models, transfer

pic models. The Knowledge based models appreciated because it d

training or domain experts which were expensive to found. And also the data wa

which are frequently available of social media and other review websites.

hao et al. [Yan, 2015] proposed an aspect-object alignment to solve

step framework for the sentiment analysis task. First one wa

alignment classifier that has basic, relational, and special target features. Complicated features we

College

Management

Sysem

Administration

System

• Admission

• Fees

• Details of staff and student

maintainence

Staff

Management

System

• Number of Staff

• Salary

Student

Management

system

• Number of Students

• Attendence

Library

Management

System

• Total no.of books

• Department wise

books

Controller of

Examination

System

Question paper

• Result


ly - 2016 [ISSN: 2455-1457]

15

Jose L. Hurtado et al. [Jos, 2016] proposed topic discovery and forecasting framework. It used an

emporal correlation analysis. This

a network of topics with its

proposed to predict the popularity of

collected from scientific papers published

SDM, and ICML. They used both the abstract and title of each

in 6122 papers as the dataset. Experiments confirmed the

better performance, with respect to the R-squared

Razdan et al. [Kal, 2015] proposed methodology diagram to evaluate document level

btaining aspect level sentiment score and its weightages. The aspect based

compared to sentiment analysis at direct

aspect sentiment analysis providing close-grained view of

to compute document level faculty performance score

re taken from the NAAC


The importance of these aspects towards computation of document level score was taken based upon

more accurate document level sentiment scores than the


t through adjective, adverb, verb, and noun. The extracted features and

re useful for generating a meaningful summary that provide significant informative

extensions of topic models that focus on problems of

based Sentiment Analysis. The extended topic models used for Aspect-based sentiment

supervised, hybrid models, transfer learning and

pic models. The Knowledge based models appreciated because it didn’t require

the data was extracted freely

object alignment to solve the object neglect

sis task. First one was an aspect-object

features. Complicated features were


Volume 02, Issue 07; July - 2016 [ISSN: 2455-1457]


resolved by imposing two types of constraints such as intra-sentence constraints and inter-sentence

constraints. Integer Linear Programming (ILP) was used as an inference procedure to obtain a final

global decision that was consistent with the constraints. Experimentation had done in camera domain

and it showed an aspect-object alignment classifier was effective in improving its performance. The

classifier along with ILP inference performed better.

Dim En et al. [Dim, 2014] proposed a feature-based summary of a product for large number of

reviews. It captured the actual relations of product features in sentences. The polarity and score of all

the features were determined by Senti-Word Net and the opinion was strong for both positive and

negative features.

Brindha V et al. [Bri, 2015] proposed a method to extract features and consider similar feature

referring same meaning. Extraction of product feature was accurate by using Mutual Reinforcement,

and Wordnet was also used to group similar feature as same meaning and opinion phrase conversion.

Senti-wordnet finally classified sentiment into positive, negative and neutral. The experimental

corpus contained publicly available consumer reviews of ten popular products in five domains.

Jie Yang et al. [Jie, 2016] proposed Douban-Learning framework for finding out critical patterns of

behavior in Chinese User Generated Content (UGC). The proposed framework consisted three main

modules, namely Data crawler module, Feature generation module and Content mining module. This

framework was implemented using the Hadoop, which was used as the fundamental tool for storing

and processing data sets. Thirteen high-level features were generated using aggregation functions.

An improved parallel Apriori algorithm was proposed to discover significant correlations among

these thirteen key features. The proposed algorithm used Spark which improved its performance and

average execution time (84.02 s) slightly slower.

III. PROPOSED WORK

Examination Management system (EMS) is an integral part of any academic institution. The exam

processing is the ultimate method for the evaluation of the student in the education system. The

results describe the performance of the student in each subject and its exam attributes. EMS is

focused to provide the interface to manage semester results. Currently administrators are facing the

problem of managing the examinations. The EMS manages the examination results in a structured

and systematic way. Producing fine graduates with glorious rankings and percentage are the main

agenda of an EMS. Feedbacks or comments or reviews are posted by staffs and students the about

the semester examination. These reviews consists of various aspects or parameters such as, question

paper, subject, content, preparation, recollection (remember), etc. The reviews are also expressed

with some sentiment keywords. This needs a lot of manual efforts to analyze the result’s feedback

and make a decision on that. An aspect based result analysis framework is shown in Fig. 2.




Fig. 2 Framework for Result analysis

The detailed description of the framework is given as pseudo code in Fig.3.

Fig.3. Pseudo code for Aspect based result analysis

Pseudo code

Input: Document reviews

Output: Aspect wise polarity

//pve=positive, nve=negative

//aw1=aspect word1, aspect word2,aspect word3,aspect

word4

1. Insert review document

2. Preprocess

3. if (‘.’ |‘;’) then

3.1 divide the document into sentences

3.2 count the number of sentences

3.3 end

4. Apply POS tagging

5. Initial aspect values are to be zero.

6. Foreach word in a sentence

7. if (word= subject | preparation |recall |content) then

7.1 assign each aspect with temporary variables

//aw1=subject; aw2=preparation; aw3=recall;

aw4=content

7.2 Count the number of occurrence of each aspect by

incrementing by 1

//aw1=aw1+1; aw2=aw2+1;aw3=aw3+1; aw4=aw4+1

7.3 end if

8. if word=aw1 && word+1=adverb|adjective

// look up in the opinion dictionary

8.1 word+1=pve

8.2 pve++;

9. else

9.1 nve++

Data collection

and preprocessing

Aspect

extraction

Aspect dictionary

(Subject,

preparation,

Opinion

extraction Opinion

dictionary

Opinion

summarization

Divide the document

into sentences and

apply POS tagging




Comments

The comments are given as feedbacks or reviews which are posted by staff and students about the

examination results.

Data Collection and Preprocessing

Feedbacks / comments are collected from the staff and students that are based on the exam results of

the college. They give their reviews or feedbacks on any aspect of a result. Preprocessing deals with

removing irrelevant attributes, removing noise, handle missing values, in order to make the data

ready for the analysis.

Aspect Extraction

Each statement is given with an intention to represent their attitude or emotion about result or aspects

of result. The features from the reviews given by the staff and students are extracted like preparation,

recollect, subject, and content. Each review compared with the aspect dictionary to identify the

aspects. Table1. Example of Aspect words with its possible opinions

Aspect words Positive opinion Negative opinion

Subject Easy, simple Tough, hard, difficult, complex, complicated

Preparation studied, learned, prepared, read not prepared, unstudied, not learned

Recall Remember, remind, recollect Forget, fail to remember

Opinion Identification

Opinions are identified with its aspects and compared with opinion dictionary. The opinions of

words are usually associated with aspects (opinion of targets) in the review documents.

Aspect and Opinion Summarization

This module aggregates the scores of each aspect with its opinion and produces an aspect based

summary. Positive and negative scores of aspects are aggregated separately.

IV. CONCLUSION

This paper analyses the comment on result of the semester exam given by teachers and students. This

proposed system considers subject, preparation, recollect and content. The proposed system also

calculates the average polarity of all comments which represents their sentiment degrees.

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Classification”, International Journal of Emerging Technology in Computer Science & Electronics

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3. Jie Yang and Brian Yecies “Mining Chinese social media UGC: a big data framework for analyzing Douban

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