Zaheer A. Gondal

Work place: School of Systems and Technology University of Management and Technology Lahore, Pakistan, School of Systems and Technology University of Management and Technology Lahore, Pakistan, Department of Computer Science Comsats University Islamabad, Lahore Campu



Research Interests: Applied computer science, Computational Science and Engineering, Computer systems and computational processes, Theoretical Computer Science


Zaheer A. Gondal is PhD Scholar/Lecturer with over 10 years of experience in diversified projects; Planned and managed projects related to community development and aligning organizational goals with technology solutions to drive process improvements, competitive advantage, and bottom-line gains. Able to manage project teams and known for high-quality deliverables that meet timeline and budgetary targets. Extensive field work, conducting assessments, surveys, monitoring and documentation of education, health, community mobilization and training related interventions in community based projects.

Author Articles
Sentiment Analysis on Mobile Phone Reviews Using Supervised Learning Techniques

By Momina Shaheen Shahid M. Awan Nisar Hussain Zaheer A. Gondal

DOI:, Pub. Date: 8 Jul. 2019

Opinion Mining or Sentiment Analysis is the process of mining emotions, attitudes, and opinions automatically from speech, text, and database sources through Natural Language Processing (NLP). Opinions can be given on anything. It may be a product, feature of a product or any sentiment view on a product. In this research, Mobile phone products reviews, fetched from, are mined to predict customer rating of the product based on its user reviews. This is performed by the sentiment classification of unlocked mobile reviews for the sake of opinion mining. Different opinion mining algorithms are used to identify the sentiments hidden in the reviews and comments for a specific unlocked mobile. Moreover, a performance analysis of Sentiment Classification algorithms is performed on the data set of mobile phone reviews. Results yields from this research provide the comparative analysis of eight different classifiers on the evaluation parameters of accuracy, recall, precision and F-measure. The Random Forest Classifiers offers more accurate predictions than others but LSTM and CNN also give better accuracy.

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