Dr. Ussama Yaqub is Assistant Professor in the Suleman Dawood School of Business at the Lahore University of Management Sciences. As a Fulbright scholar, he obtained his PhD in Management Information Systems from Rutgers Business School, Rutgers the State University of New Jersey.
His research focuses on social media data analyses, including sentiment and behavior analyses, data mining and studying the use of Twitter for campaigning during general elections. Ussama has published in international journals and conference proceedings such as Government Information Quarterly, ACM Digital Government: Research & Practice and ACM Digital Government Research.
Ussama holds MBA from LUMS and has six years of work experience in the areas of Business Intelligence and Data warehousing. Prior to his PhD, he had been working as Assistant Manager - Micro-Segmentation in the Business Intelligence department at Telenor Pakistan and as a consultant in Teradata Global Consulting Centre.
At SDSB Ussama teaches courses in Undergraduate and MBA programs in the area of Decision Sciences, Business Intelligence and Marketing.
Yaqub, U. (2020). Tweeting during Covid-19 Pandemic: Sentiment Analysis of Twitter Messages by President Trump. Digital Government: Research and Practice, doi:10.1145/3428090.
YAQUB, U., SHARMA, N., PABREJA, R., CHUN & CHUN, S. & ATLURI, V. (2020). Location-based Sentiment Analyses and Visualization of Twitter Election Data. Digital Government: Research and Practice.
Yaqub, U., Chun, S., Atluri, V. & Vaidya & Vaidya, J. (2017). Analysis of political discourse on twitter in the context of the 2016 US presidential elections. Government Information Quarterly, 34 (4), 14.
Soomro, Z., Ilyas, S. & Yaqub, U. (2020). Sentiment, Count and Cases: Analysis of Twitter discussions during COVID-19 Pandemic. 2020 7th International Conference on Behavior, Economic and Social Computing (BESC) IEEE.
Yaqub, U., Malik, M. & Zaman, S. (2020). Sentiment Analysis of Russian IRA Troll Messages on Twitter during US Presidential Elections of 2016. 7th International Conference on Behavior, Economic and Social Computing (BESC) IEEE.
Anwar, A. & Yaqub, U. (2020). Bot detection in twitter landscape using unsupervised learning. dg.o '20: The 21st Annual International Conference on Digital Government Research.
Ilyas, S., Soomro, Z., Anwar, A., Shahzad & Shahzad, H. & Yaqub, U. (2020). Analyzing Brexit's impact using sentiment analysis and topic modeling on Twitter discussion. dg.o '20: The 21st Annual International Conference on Digital Government Research, doi:https://doi.org/10.1145/3396956.3396973.
Yaqub, U., Chun, S., Atluri, V. & Vaidya & Vaidya, J. (2019). Social Media Communication of Public Sector Organizations: A Case Study of Northeast US. dg.o 2019: Proceedings of the 20th Annual International Conference on Digital Government Research.
Sharma, N., Pabreja, R., Yaqub, U., Atluri & Atluri, V. & Chun, S. (2018). Web-based application for sentiment analysis of live tweets. dg.o '18 Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, doi:10.1145/3209281.3209402.
Yaqub, U., Sharma, N., Pabreja, R., Chun & Chun, S. & Atluri, V. (2018). Analysis and Visualization of Subjectivity and Polarity of Twitter Location Data. dg.o '18 Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, 10, doi:10.1145/3209281.3209313.
Yaqub, U., Chun, S., Atluri, V. & Vaidya & Vaidya, J. (2017). Sentiment based Analysis of Tweets during the US Presidential Elections. Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age.
Yaqub, U., Atluri, V. & Vaidya, J. (2016). Efficient Evaluation of Authorizations for Video Data. Proceedings of the 9th International Conference on Security of Information and Networks, doi:10.1145/2947626.2947652.