Muhammad Zubair Asghar

Muhammad Zubair Asghar, Fazli Subhan, Muhammad Imran et al.

Emotion detection from the text is an important and challenging problem in text analytics. The opinion-mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification.

Aurangzeb khan, Khairullah khan, Shakeel Ahmad et al.

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% and83% at document and sentence levels respectively for online comments.

Muhammad Zubair Asghar, Ammara Habib, Anam Habib et al.

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

Muhammad Zubair Asghar, Shakeel Ahmad, Afsana Marwat et al.

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.