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A Unified Framework for Creating Domain Dependent Polarity Lexicons from User Generated Reviews

Muhammad Zubair Asghar
Institute of Computing and Information Technology (ICIT), Gomal University, Dera Ismail Khan, Pakistan
Journal Paper PLOS ONE, 00/2015, Pages e0140204 Vol 10 Issue 10

Abstract

The exponential increase in the explosion of Web-based user generated reviews has resulted in the emergence of Opinion Mining (OM) applications for analyzing the users’ opinions toward products, services, and policies. The polarity lexicons often play a pivotal role in the OM, indicating the positivity and negativity of a term along with the numeric score. However, the commonly available domain independent lexicons are not an optimal choice for all of the domains within the OM applications. The aforementioned is due to the fact that the polarity of a term changes from one domain to other and such lexicons do not contain the correct polarity of a term for every domain. In this work, we focus on the problem of adapting a domain dependent polarity lexicon from set of labeled user reviews and domain independent lexicon to propose a unified learning framework based on the information theory concepts that can assign the terms with correct polarity (+ive, -ive) scores. The benchmarking on three datasets (car, hotel, and drug reviews) shows that our approach improves the performance of the polarity classification by achieving higher accuracy. Moreover, using the derived domain dependent lexicon changed the polarity of terms, and the experimental results show that our approach is more effective than the base line methods.

Lexicon based Approach for Sentiment Classification of User Reviews

Muhammad Zubair Asghar,Irfan ullah Nawaz , Rahman ullah, Shakeel Ahmad, Fazal Masud Kundi
Institute of Computing and Information Technology Gomal University Dera Ismail Khan
Journal Paper Life Science Journal, 00/2014, Pages 468-473 Vol 11 Issue 10

Abstract

With the advent of web, online user reviews are getting more and more attention of the researchers because valuable information about products and services are available on social media like twitter 1. These reviews are very helpful for organizations as well as for new customers showing interest in these products or services. But this data is generated in tremendous amount which is out of control of manual mining methods. These reviews need a model that has the ability to gauge these shared reviews according to predefined categories. This work introduces a rule based approach to find the opinion classification of reviews. The system can automatically crawl reviews from social media sites, classify these reviews as subjective and objective and then calculate polarity score for subjective reviews at word level. This method shows impressive results and out-performs the baseline method by achieving 86% and 82% accuracy at feedback and sentence level respectively for comments and 96% at feedback and 85 % at sentences for reviews.

Detection and Scoring of Internet Slangs for Sentiment Analysis Using SentiWordNet

Muhammad Zubair Asghar, Fazal Masud Kundi, Shakeel Ahmad, Aurangzeb Khan
·Institute of Computing and Information Technology, Gomal University, D.I. Khan, Pakistan,(3)Institute of Engineering and Computer Sciences, University of Science and Technology Bannu, Pakistan
Journal Paper LIFE SCIENCE JOURNAL , 00/2014, Pages 66-72 Vol 11 Issue 9

Abstract

The online information explosion has created great challenges and opportunities for both information producers and consumers. Understanding customer’s feelings, perceptions and satisfaction is a key performance indicator for running successful business. Sentiment analysis is the digital recognition of public opinions, feelings, emotions and attitudes. People express their views about products, events or services using social networking services. These reviewers excessively use Slangs and acronyms to express their views. Therefore, Slang's analysis is essential for sentiment recognition. This paper presents a framework for detection and scoring of Internet Slangs (DSIS) using SentiWordNet in conjunction with other lexical resources. The comparative results show that proposed system outperforms the existing systems . [Fazal Masud Kundi, Shakeel Ahmad, Aurangzeb Khan, Muhammad Zubair Asghar. Detection and Scoring of Internet Slangs for Sentiment Analysis Using SentiWordNet. Life Sci J 2014;11(9):66-72]. (ISSN:1097-8135). Detection and Scoring of Internet Slangs for Sentiment Analysis Using SentiWordNet (PDF Download Available). Available from: https://www.researchgate.net/publication/283318703_Detection_and_Scoring_of_Internet_Slangs_for_Sentiment_Analysis_Using_SentiWordNet [accessed Apr 7, 2016].

Sentiment Classification through Semantic Orientation Using SentiWordNet

Muhammad Zubair Asghar,Furqan Khan, Aurangzeb khan , Shakeel Ahmad, Fazal Masud Kundi, Maria Qasim
Institute of Computing and Information Technology (ICIT), Gomal University, Dera Ismail Khan, Pakistan
Journal Paper Life Science Journal, 00/2014, Pages 309-315 Vol 11 Issue 10

Abstract

Sentiment analysis is the procedure by which information is extracted from the opinions, appraisals and emotions of people in regards to entities, events and their attributes. In decision making, the opinions of others have a significant effect on customers ease in making choices regards to online shopping, choosing events, products, entities. In this paper, arule based domain independent sentiment analysis method is proposed. The proposed method classifies subjective and objective sentences from reviews and blog comments. The semantic score of subjective sentences is extracted from SentiWordNet to calculate their polarity as positive, negative or neutral based on the contextual sentence structure. The results show the effectiveness of the proposed method and it outperforms the machine learning methods. The proposed method achieves an accuracy of 87% at the feedback level and 83% at the sentence level for comments.

A Review of Feature Extraction in Sentiment Analysis

Muhammad Zubair Asghar, Aurangzeb Khan, Shakeel Ahmad, Fazal Masud Kundi
Institute of Computing and Information Technology (ICIT), Gomal University, Dera Ismail Khan, Pakistan (2) Institute of Engineering and Computer Sciences, University of Science and Technology Bannu, Pakistan
Journal Paper Journal of Basic and Applied Scientific Research, 00/2014, Pages 181-186 Vol 4 Issue 3

Abstract

Rapid increase in internet users along with growing power of online review sites and social media has given birth to Sentiment analysis or Opinion mining, which aims at determining what other people think and comment. Sentiments or Opinions contain public generated content about products, services, policies and politics. People are usually interested to seek positive and negative opinions containing likes and dislikes, shared by users for features of particular product or service. Therefore product features or aspects have got significant role in sentiment analysis. In addition to sufficient work being performed in text analytics, feature extraction in sentiment analysis is now becoming an active area of research. This review paper discusses existing techniques and approaches for feature extraction in sentiment analysis and opinion mining. In this review we have adopted a systematic literature review process to identify areas well focused by researchers, least addressed areas are also highlighted giving an opportunity to researchers for further work. We have also tried to identify most and least commonly used feature selection techniques to find research gaps for future work.

Medical opinion lexicon: an incremental model for mining health reviews

Muhammad Z Asghar, Aurangzeb Khan, Fazal M Kundi, Maria Qasim, Furqan Khan, Rahman Ullah, Irfan U Nawaz
Institute of Computing and Information Technology, Gomal University, D.I.Khan,(2) University of Science and Technology Bannu(PAKISTAN)
Journal Paper International Journal of Academic Research, 00/2014, Pages 295-302 Vol 6 Issue 1

Abstract

Opinion mining concentrates on retrieving opinions of online users about a service, product, and policy. In this paper, we propose a medical opinion lexicon for mining health reviews available on different health forums. The proposed technique is based on the incremental modal and corpus of health reviews by creating medical polarity lexicon for medical terms. In each increment, vocabulary of lexicon is enhanced systematically, polarity score with each word is attached, and finally, resulting lexicon is filtered from unnecessary words by using word sense disambiguation techniques. The comparative results show the efficiency of proposed method and it outperforms the existing approaches.The proposed approach achieves an accuracy of 82% on training corpus and 78% on testing corpus of health reviews.

A Review of Location Technologies for Wireless Mobile Location-Based Services

MUHAMMAD ZUBAIR ASGHAR,SHAKEEL AHMAD,MUHAMMAD RAMZAN YASIN,MARIA QASIM
Institute of Computing and Information Technology, Gomal University, D.I.Khan, (Pakistan)
Journal Paper Journal of American Science, 00/2014, Pages 110-118 Vol 10 Issue 7

Abstract

The demand of mobile data services has been increased dramatically with the improvement in wireless mobile technologies from past few years. Wireless mobile network operator provide many different kind of applications to gain attention of their valuable users, some of these are, downloading of ring tone, songs, wallpapers, transmitting of short and multimedia messages and video clips etc. The information about the location of the user is used for the purpose of providing the better kind of services to the user of the wireless mobile network. This type of applications which uses the location of user of the wireless mobile network is termed as Wireless-Location-Bases-Services (WLBS) by the service provider, which will increase the revenue for the wireless mobile network operator and very useful for the customer of these services in near future. But providing these services the wireless mobile network operator must addresses the different issues involved, comprising the development in technology used for, approval of user privacy, standardization and the accessibility of smart services. Various Wireless-Location-Based-Services (WLBS) engage the variety of factors for revenue generated smart services. This paper provides a review of current development and prerequisites for the purpose of providing Wireless-Location-Based-Services (WLBS) and its installation on UMTS, GPRS and GSM wireless mobile networks.

Lexicon-Based Sentiment Analysis in the Social Web

Fazal Masud Kundi , Aurangzeb Khan , Shakeel Ahmad , Muhammad Zubair Asghar
Institute of Computing and Information Technology, Gomal University, D.I.Khan, Pakistan, (2) Institute of Engineering and Computer Sciences, University of Science and Technology Bannu, Pakistan
Journal Paper Journal of Basic and Applied Scientific Research, 00/2014, Pages 238-248 Vol 4 Issue 6

Abstract

Sentiment analysis is a compelling issue for both information producers and consumers. We are living in the “age of customer”, where customer knowledge and perception is a key for running successful business. The goal of sentiment analysis is to recognize and express emotions digitally. This paper presents the lexicon-based framework for sentiment classification, which classifies tweets as a positive, negative, or neutral. The proposed framework also detects and scores the slangs used in the tweets. The comparative results show that the proposed system outperforms the existing systems. It achieves 92% accuracy in binary classification and 87% in multi-class classification. KEYWORDS: Opinion Mining, Lexicon, Tweets, Social media, Semantic Orientation

Lexical Based Semantic Orientation of Online Customer Reviews and Blogs

Muhammad Zubair Asghar, Aurangzeb khan, Khairullah khan, Shakeel Ahmad, Fazal Masood Kundi, Irum Tareen
(2) Institute of Engineering and Computing Sciences, University Of Science and Technology Bannu, Pakistan, Institute of Computing and Information Technology, Gomal University D. I. khan, Pakistan.
Journal Paper Journal of American Science, 00/2014, Pages 143-147 Vol 10 Issue 8

Abstract

Rapid increase in internet users along with growing power of online review sites and social media has given birth to sentiment analysis or opinion mining, which aims at determining what other people think and comment. Sentiments or Opinions contain public generated content about products, services, policies and politics. People are usually interested to seek positive and negative opinions containing likes and dislikes, shared by users for features of particular product or service. This paper proposed sentence-level lexical based domain independent sentiment classification method for different types of data such as reviews and blogs. The proposed method is based on general lexicons i.e. WordNet, SentiWordNet and user defined lexical dictionaries for semantic orientation. The relations and glosses of these dictionaries provide solution to the domain portability problem. The method performs better than word and text level corpus based machine learning methods for semantic orientation. The results show the proposed method performs better as it shows precision of 87% and 83% at document and sentence levels respectively for online comments.

Financial Studio: Android Based Application for Computing Tax, Pension, Zakat and Loan

Muhammad Zubair Asghar, Ulfat Batool, Farheen Bibi, Sadia Ismail, Rabail Zahra
Institute of Computing and Information Technology Gomal University, Dera Ismail Khan, Pakistan
Journal Paper International Journal of Academic Research [ISSN: 2075-4124], 00/2016, Pages 96-117 (22) Vol 2 Issue 4

Abstract

This work deals with the development of android-based financial studio, an integrated application for calculating tax, pension, zakat, and loan. Financial studio can facilitate employers of any department and other individuals. The application is developed using MIT app inventor-based android platform. The financial studio has four computational modules, namely: (i) tax, (ii) pension, (iii) zakat, and (iv) loan. The system provides an integrated environment for performing aforementioned distinct calculations by integrating different financial modules into a single application in a user-friendly way. The statistical analysis shows that the application is effective to deal with different financial calculations. Keywords: Financial studio, MIT app inventor, android, tax calculator, pension calculator, zakat calculator, loan calculator

Sentiment Analysis on YouTube: A Brief Survey

Muhammad Zubair Asghar, Shakeel Ahmad, Afsana Marwat, Fazal Masud Kundi
Institute of Computing and Information Technology (ICIT), Gomal University, D. I. Khan, Pakistan.(2) Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdul Aziz University (KAU) Saudi Arabia.
Journal Paper MGT Research, 00/2015, Pages 1250-1257 Vol 3 Issue 1

Abstract

Sentiment analysis or opinion mining is the field of study related to analyze opinions, sentiments, evaluations, attitudes, and emotions of users which they express on social media and other online resources. The revolution of social media sites has also attracted the users towards video sharing sites, such as YouTube. The online users express their opinions or sentiments on the videos that they watch on such sites. This paper presents a brief survey of techniques to analyze opinions posted by users about a particular video.

AndorEstimator: Android based Software Cost Estimation Application

Muhammad Zubair Asghar, Ammara Habib, Anam Habib, Rabail Zahra, and Sadia Ismail
Institute of Computing and Information Technology Gomal University, Dera Ismail Khan, Pakistan
Journal Paper International Journal of Computer Science and Information Security (IJCSIS) ISSN 1947-5500, 00/2016, Pages 192 - 202 Vol 4 Issue 14

Abstract

The main aim of the proposed system is to assist the software development team to estimate the cost, effort and maintenance of the project under development. Android-based platform, namely MIT App Inventor is used for the development of application, which contains visual block programming language. The current study has following uniqueness of (1)Accuracy of results,(2)user friendly environment(3)no such application is available on android platform to the best of our knowledge. Questionnaire regarding CoCoMo model is developed and circulated by using objective qualitative method. Findings: The estimation module of our application is quite important with respect to facilitating the students of software engineering for performing CoCoMo-based cost estimation easily, and enabling the software developers for performing software cost estimation easily. The cost estimator based on CoCoMo model is developed on android platform however, to the best of our knowledge no such application is available. This system can be used by business and educational stakeholders, such as students, software developers, and business organizations Keywords CoCoMo model; App Inventor; Cost estimation; Android

Quizzes: Quiz Application Development Using Android-Based MIT APP Inventor Platform

Muhammad Zubair Asghar, Iqra Sana, Kushbo Nasir, Hina Iqbal, Fazal Masud Kundi, Saida Ismail
Institute of Computing and Information Technology Gomal University, Dera Ismail Khan, Pakistan
Journal Paper International Journal of Advanced Computer Science and Applications (IJACSA), 00/2016, Pages 12 (43-54) Vol 5 Issue 7

Abstract

This work deals with the development of Android-based multiple-choice question examination system, namely: Quizzes. This application is developed for educational purposes, allowing the users to prepare the multiple choice questions for different examinations conducted on provincial and national level. The main goal of the application is to enable users to practice for subjective tests conducted for admissions and recruitment, with the focus on Computer Science field. This quiz application includes three main modules, namely (i) computer science, (ii) verbal, and (iii) analytical. The computer science and verbal modules contain various sub-categories. This quiz includes three functions: (i) Hint, (ii) Skip, and (iii) Pause/life-lines. These functions can be used only once by a user. It shows progress feedback during quiz play, and at the end, the app also shows the result.

Inheritance Evaluation System using Islamic law

Muhammad Zubair Asghar, Fazal Masud Kundi, Abdur Rashid Khan
Institute of Computing and Information Technology Gomal University, Dera Ismail Khan, Pakistan
Journal Paper Journal of Higher Education Institutions, Izvestia Vozov, National Attestation Commission, Publishing Center «???», “Journal of Higher Education Institutions Reports”, Isanov-87, Bishkek, K.R, 00/2004, Pages 163-171(9) Vol 9 Issue 6

Abstract

The research work about the Inheritance Evaluation System using Islamic law is valuable for automatic calculation of share out of total inheritance of a deceased to his/her legal heir(s). First version of the software named as Islamic Inheritance Evaluation System (IIES) deals with Hanfi School of thought. IIES may solve the heritage problem of heirs in text as well as in graphical form at home without establishing a suit in any court. This also leads to further research of who is how much related to whom?

Simplified Neural Network Design for Hand Written Digit Recognition

Muhammad Zubair Asghar, Hussain Ahmad, Shakeel Ahmad, Sheikh Muhammad Saqib, Bashir Ahmad and Muhammad Junaid Asghar
Institute of Computing and Information Technology Gomal University, Dera Ismail Khan, Pakistan
Journal Paper International Journal of Computer Science and Information Security (IJCSIS) ISSN 1947-5500, 00/2011, Pages 319-322(4) Vol 9 Issue 6

Abstract

In this work a very simple and flexible neural network scheme is proposed and implemented for handwritten digit recognition, which will assist beginners and A.I students who want to understand perceptive capability of neural network. In the proposed system, a very simple design of artificial neural networks is implemented. First of all learning mechanism of the neural network is described and then its architecture is discussed. Proposed network is trained in supervised manner using various (approx: 250) patterns /fonts of handwritten digits. Unique token is allocated to digit when it is made input to the system. Network becomes adaptive when different patterns of the same digit are taught to the network for one particular token.

SentiHealth: creating health-related sentiment lexicon using hybrid approach

Muhammad Zubair Asghar,Shakeel Ahmad, Maria Qasim, Rabail Zahra, Fazal Masud Kundi
Institute of Computing and Information Technology Gomal University, Dera Ismail Khan, Pakistan
Journal Paper SpringerPlus, 00/2016, Pages 1-23 Vol 10.1186/s40064-016-2809-x Issue 0

Abstract

The exponential increase in the health-related online reviews has played a pivotal role in the development of sentiment analysis systems for extracting and analyzing user-generated health reviews about a drug or medication. The existing general purpose opinion lexicons, such as SentiWordNet has a limited coverage of health-related terms, creating problems for the development of health-based sentiment analysis applications. In this work, we present a hybrid approach to create health-related domain specific lexicon for the efficient classification and scoring of health-related users’ sentiments. The proposed approach is based on the bootstrapping modal, a dataset of health reviews, and corpus-based sentiment detection and scoring. In each of the iteration, vocabulary of the lexicon is updated automatically from an initial seed cache, irrelevant words are filtered, words are declared as medical or non-medical entries, and finally sentiment class and score is assigned to each of the word. The results obtained demonstrate the efficacy of the proposed technique. Journal Name: SpringerPlus, Impact Factor: 0.98

Lexicon-enhanced sentiment analysis framework using rule-based classification scheme

Muhammad Zubair Asghar, Aurangzeb Khan, Shakeel Ahmad, Maria Qasim, Imran Ali Khan
ICIT
Journal Paper PLOS ONE, 00/2017, Pages 22 Vol Issue 0

Abstract

With the rapid increase in social networks and blogs, the social media services are increasingly being used by online communities to share their views and experiences about a particular product, policy and event. Due to economic importance of these reviews, there is growing trend of writing user reviews to promote a product. Nowadays, users prefer online blogs and review sites to purchase products. Therefore, user reviews are considered as an important source of information in Sentiment Analysis (SA) applications for decision making. In this work, we exploit the wealth of user reviews, available through the online forums, to analyze the semantic orientation of words by categorizing them into +ive and -ive classes to identify and classify emoticons, modifiers, general-purpose and domain-specific words expressed in the public’s feedback about the products. However, the un-supervised learning approach employed in previous studies is becoming less efficient due to data sparseness, low accuracy due to non-consideration of emoticons, modifiers, and presence of domain specific words, as they may result in inaccurate classification of users’ reviews. Lexicon-enhanced sentiment analysis based on Rule-based classification scheme is an alternative approach for improving sentiment classification of users’ reviews in online communities. In addition to the sentiment terms used in general purpose sentiment analysis, we integrate emoticons, modifiers and domain specific terms to analyze the reviews posted in online communities. To test the effectiveness of the proposed method, we considered users reviews in three domains. The results obtained from different experiments demonstrate that the proposed method overcomes limitations of previous methods and the performance of the sentiment analysis is improved after considering emoticons, modifiers, negations, and domain specific terms when compared to baseline methods.

RIFT: A Rule Induction Framework for Twitter Sentiment Analysis

Muhammad Zubair Asghar, Furqan Khan, Fazal Masud Kundi
ICIT, Gomal University
Journal Paper Springer, 00/2017, Pages 21 Vol Issue 0

Abstract

The rapid evolution of microblogging and the emergence of sites such as Twitter have propelled online communities to flourish by enabling people to create, share and disseminate free-flowing messages and information globally. The exponential growth of product-based user reviews has become an ever-increasing resource playing a key role in emerging Twitter-based sentiment analysis (SA) techniques and applications to collect and analyse customer trends and reviews. Existing studies on supervised black-box sentiment analysis systems do not provide adequate information, regarding rules as to why a certain review was classified to a class or classification. The accuracy in some ways is less than our personal judgement. To address these shortcomings, alternative approaches, such as supervised white-box classification algorithms, need to be developed to improve the classification of Twitter-based microblogs. The purpose of this study was to develop a supervised white-box microblogging SA system to analyse user reviews on certain products using rough set theory (RST)-based rule induction algorithms. RST classifies microblogging reviews of products into positive, negative, or neutral class using different rules extracted from training decision tables using RST-centric rule induction algorithms. The primary focus of this study is also to perform sentiment classification of microblogs (i.e. also known as tweets) of product reviews using conventional, and RST-based rule induction algorithms. The proposed RST-centric rule induction algorithm, namely Learning from Examples Module version: 2, and LEM2 ++ Corpus-based rules (LEM2 ++ CBR),which is an extension of the traditional LEM2 algorithm, are used. Corpus-based rules are generated from tweets, which are unclassified using other conventional LEM2 algorithm rules. Experimental results show the proposed method, when compared with baseline methods, is excellent, with regard to accuracy, coverage and the number of rules employed. The approach using this method achieves an average accuracy of 92.57% and an average coverage of 100%, with an average number of rules of 19.14.

T-SAF: Twitter sentiment analysis framework using a hybrid classification scheme

Muhammad Zubair Asghar, Fazal Masud Kundi, Shakeel Ahmad, Aurangzeb Khan, Furqan Khan
ICIT, Gomal University
Journal Paper Wiley, 00/2017, Pages 1-19 Vol Issue 0

Abstract

Of the many social media sites available, users prefer microblogging services such as Twitter to learn about product services, social events, and political trends. Twitter is considered an important source of information in sentiment analysis applications. Supervised and unsupervised machine learning-based techniques for Twitter data analysis have been investigated in the last few years, often resulting in an incorrect classification of sentiments. In this paper, we focus on these issues and present a unified framework for classifying tweets using a hybrid classification scheme. The proposed method aims at improving the performance of Twitter-based sentiment analysis systems by incorporating 4 classifiers: (a) a slang classifier, (b) an emoticon classifier, (c) the SentiWordNet classifier, and (d) an improved domain-specific classifier. After applying the preprocessing steps, the input text is passed through the emoticon and slang classifiers. In the next stage, SentiWordNet-based and domain-specific classifiers are applied to classify the text more accurately. Finally, sentiment classification is performed at sentence and document levels. The findings revealed that the proposed method overcomes the limitations of previous methods by considering slang, emoticons, and domain-specific terms.

Aspect-based opinion mining framework using heuristic patterns

Muhammad Zubair Asghar, Auangzeb Khan, Rabail Zahra, Shakeel Ahmad, Fazal Masud Kundi
ICIT, Gomal University
Journal Paper Cluster Computing (Springer), 00/2017, Pages 1-19 Vol Issue 0

Abstract

The aspect-based online opinions expressed by users on social media sites have become a popular source of information for consumers regarding their purchase decisions as well as for companies seeking opinions on their products. Therefore, it is important to develop aspect-based opinion mining applications with an emphasis on extracting and classifying the aspect-based opinions expressed by users about products in a given review. Previous studies have used a limited set of heuristic patterns for aspect extraction with both supervised (annotated-dataset-based) and unsupervised (lexical-resource-based) aspect-related sentiment classification algorithms. However, the present study proposes an integrated framework comprising of an extended set of heuristic patterns for aspect extraction, a hybrid sentiment classification module with the additional support of intensifiers and negations, and a summary generator. The performance evaluation of the proposed aspect-based opinion mining system using state-of-the-art methods shows that the proposed system outperforms the alternative methods in terms of better precision, recall and F-measure, since it achieves an average precision of 85%, an average recall of 73% and an average F-measure of 0.78. The comparative results indicate that the proposed technique provides more efficient results for the aspect-sentiment extraction, classification and summary generation of online product reviews.

Sentence-Level Emotion Detection Framework Using Rule-Based Classification

Muhammad Zubair Asghar, Auangzeb Khan, Afsana Bibi, Fazal Masud Kundi, Hussain Ahmad
ICIT, Gomal University
Journal Paper Cognitive Computation (Springer), 00/2017, Pages 1-27 Vol Issue 0

Abstract

Emotion detection and analysis aims at developing applications that can detect and analyse emotions expressed by the users in a given text. Such applications have received considerable attention from experts in computer science, psychology, communications and health care. Emotion-based sentiment analysis can be performed using supervised and unsupervised techniques. The existing studies using supervised and unsupervised emotion-based sentiment analysis are based on Ekman’s basic emotion model; have limited coverage of emotion-words, polarity shifters and negations; and lack emoticons and slang. The problems associated with existing approaches can be overcome by the development of an effective, sentence-level emotion-detection sentiment analysis system under a rule-based classification scheme with extended lexicon support and an enhanced model of emotion signals: emotion words, polarity shifters, negations, emoticons and slang. In this work, we propose a rule-based framework for emotion-based sentiment classification at the sentence level obtained from user reviews. The main contribution of this work is to integrate cognitive-based emotion theory (e.g. Ekman’s model) with sentiment analysis-based computational techniques (e.g. detection of emotion words, emoticons and slang) to detect and classify emotions from natural language text. The main focus is to improve the performance of state-of-the-art methods by including additional emotion-related signals, such as emotion words, emoticons, slang, polarity shifters and negations, to efficiently detect and classify emotions in user reviews. The improved results in terms of accuracy, precision, recall and F-measure demonstrate the superiority of the proposed method’s classification results compared with baseline methods. The framework is generalized and capable of classifying emotions in any domain.

COGEMO: Cognitive-Based Emotion Detection from Patient Generated Health Reviews

Muhammad Zubair Asghar, Aurangzeb Khan, Khairullh Khan, Hussain Ahmad, and Imran Ali Khan
ICIT, Gomal University
Journal Paper Journal of Medical Imaging and Health Informatics ISSN: 2156-7018 (Print): EISSN: 2156-7026 (Online) , , Pages Vol Issue 0

Abstract

A Rule-Based Sentiment Classification Framework for Health Reviews on Mobile Social Media

Aurangzeb Khan, Muhammad Zubair Asghar, Hussain Ahmad, Fazal Masud Kundi, and Sadia Ismail
ICIT, Gomal University
Journal Paper Journal of Medical Imaging and Health Informatics ISSN: 2156-7018 (Print): EISSN: 2156-7026 (Online) , , Pages Vol Issue 0

Abstract

Data sets for User Reviews on Drugs

Maria Qasim
ICIT, Gomal University
Journal Paper , 00/2017, Pages Vol Issue 0

Abstract

This document provides detail of datasets used by Maria Qasim during her MS-CS research under the supervision of Dr. Muhammad Zubair Asghar

Data sets for User Reviews on Drugs

Maria Qasim
ICIT, Gomal University
Journal Paper , 00/2017, Pages Vol Issue 0

Abstract

This document provides detail of datasets used by Maria Qasim during her MS-CS research under the supervision of Dr. Muhammad Zubair Asghar

Extended Emotion Lexicon

Afsana Bibi, Dr. Muhammad Zubair Asghar
ICIT, Gomal University
Journal Paper , 00/2017, Pages Vol Issue 0

Abstract

This work is carried out by Afsana Bibi, MS-CS scholar, under the supervision of Dr. Muhammad Zubair Asghar, Assistant Professor, ICIT, Gomal University, DIKHAN, Pakistan

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