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Location based sentiment analysis is the use of natural language processing or machine learning algorithms to extract, identify, or characterize the sentiment content of a 'text unit', according to the location of origin of the text unit. In this paper, we study the application of location based sentiment analysis using Twitter for identifying trends and patterns towards the Indian general elections 2014. We perform data (text) mining on 650,000 tweets collected over a period of 5 days pertaining to two political parties in India, during the campaigning period. We make use of Naive Bayes algorithm to build our classifier and classify the test data (as positive or negative) according to it. We identify the sentiment of Twitter users towards each of the two Indian political parties, by location and plot our findings on an Indian map. In the end, we present our observations and conclusions and how certain "social events" influence the sentiments of Twitter users on the social network. We also discuss the issues related to geo-location using the data obtained from the Twitter API.

Johri studies the use of information and communication technologies (ICT) for learning and knowledge sharing, with a focus on cognition in informal environments. He also examine the role of ICT in supporting distributed work among globally dispersed workers and in furthering social development in emerging economies. He received the U.S. National Science Foundation's Early Career Award in 2009. He is co-editor of the Cambridge Handbook of Engineering Education Research (CHEER)

We present an empirical study that compared the student learning outcomes of face-to-face and distance learning sections of a Telecommunications course. Student performance was assessed based on the course grade, which included the final exam, quizzes, assignments, and midterm exam scores. Both classes were taught by the same instructor, and had similar content and assessment measures. The study factored in the students' demographics such as gender, work and residency status to assess their impact on student learning. In addition, data stored in the learning management system (LMS), BlackBoard ™, were collected and used to understand student activities within the system, and determine their relation with student performance. The number of times the material was accessed and the time duration spent on assessments are some of the examples of the data that were included in the study. The results show that there is a correlation between students' use of Blackboard and student performance. We found a significant statistical difference between course grades of the face-to-face and distance learning sections. We did not find any evidence for significant difference across a range of demographic factors.