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Abstract-Social media serves as a unified platform for users to express their thoughts on subjects ranging from their daily lives to their opinion on consumer brands and products. These users wield an enormous influence in shaping the opinions of other consumers and influence brand perception, brand loyalty and brand advocacy. In this paper, we analyze the opinion of 19M Twitter users towards 62 popular industries, encompassing 12,898 enterprise and consumer brands, as well as associated subject matter topics, via sentiment analysis of 330M tweets over a period spanning a month. We find that users tend to be most positive towards manufacturing and most negative towards service industries. In addition, they tend to be more positive or negative when interacting with brands than generally on Twitter. We also find that sentiment towards brands within an industry varies greatly and we demonstrate this using two industries as use cases. In addition, we discover that there is no strong correlation between topic sentiments of different industries, demonstrating that topic sentiments are highly dependent on the context of the industry that they are mentioned in. We demonstrate the value of such an analysis in order to assess the impact of brands on social media. We hope that this initial study will prove valuable for both researchers and companies in understanding users' perception of industries, brands and associated topics and encourage more research in this field.

In order to make context-aware systems more effective and provide timely, personalized and relevant information to a user, the context or situation of the user must be clearly defined along several dimensions. To this end, the system needs to simultaneously recognize multiple dimensions of the user's situation such as location, physical activity etc. in an automated and unobtrusive manner. In this paper, we present SenseMe-a system that leverages a user's smartphone and its multiple sensors in order to perform continuous, on-device, and multi-dimensional context and activity recognition. It recognizes five dimensions of a user's situation in a robust, automated, scalable, power efficient and non-invasive manner to paint a context-rich picture of the user. We evaluate SenseMe against several metrics with the aid of 2 two-week long live deployments involving 15 participants. We demonstrate improved or comparable accuracy with respect to existing systems without requiring any user calibration or input.

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