In recent years, social networks have become very popular. Twitter, a micro-blogging service, is estimated to have about 200 million registered users and these users create approximately 65 million tweets a day. Twitter users usually show their opinion about topics of their interest. The challenge is that each tweet is limited in 140 characters, and is hence very short. It may contain slang and misspelled words. Thus, it is difficult to apply traditional NLP techniques which are designed for working with formal languages, into Twitter domain. Another challenge is that the total volume of tweets is extremely high, and it takes a long time to process. In this paper, we describe a large-scale distributed system for real-time Twitter sentiment analysis. Our system consists of two components: a lexicon builder and a sentiment classifier. These two components are capable of running on a large-scale distributed system since they are implemented using a MapReduce framework and a distributed database model. Thus, our lexicon builder and sentiment classifier are scalable with the number of machines and the size of data. The experiments also show that our lexicon has a good quality in opinion extraction, and the accuracy of the sentiment classifier can be improved by combining the lexicon with a machine learning technique.

This paper discusses our ongoing experiences with teaching software engineering through an inverted classroom. This course format moves traditional lectures out of in-class hours and into the student's personal study time with prerecorded lectures. We support the inverted classroom with complementary techniques, such as structured discussions, weekly quizzes to ensure students watch the lectures before discussion, an innovative Lego-based workshop, a term project, and guest lectures by industry professionals. The inverted classroom allows the students to have an effective educational experience that encompasses both traditional lectures and an active learning environment. To evaluate the efficacy of this format, we use surveys and interviews of both instructors and students. We examine the time commitment of teaching with this method, from both the instructors' perspective and the students'. We also discuss the time commitment for instructor preparation, and quantitative measures of how the inverted classroom helps smooth the variance in the quality of each instructor's teaching. We also analyze the effectiveness of this technique and our methods for mitigating unintended consequences, such as students having an inexact understanding of the material. Through this evaluation, we distill the effects on student learning and instructor teaching.

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An evolving process structure executes an effective sequence of decisions while providing real-time response to incoming requests and increasing business visibility across all requests.

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