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With the proliferation of social networks, people are likely to share their opinions about news, social events and products on the Web. There is an increasing interest in understanding users' attitude or sentiment from the large repository of opinion-rich data on the Web. This can benefit many commercial and political applications. Primarily, the researchers concentrated on the documents such as users' comments on the purchased products. Recent works show that visual appearance also conveys rich human affection that can be predicted. While great efforts have been devoted on the single media, either text or image, little attempts are paid for the joint analysis of multi-view data which is becoming a prevalent form in the social media. For example, paired with the posted textual messages on Twitter, users are likely to upload images and videos which may carry their affective states. One common obstacle is the lack of sufficient manually annotated instances for model learning and performance evaluation. To prompt the researches on this problem, we introduce a multi-view sentiment analysis dataset (MVSA) including a set of manually annotated image-text pairs collected from Twitter. The dataset can be utilized as a valuable benchmark for both single-view and multi-view sentiment analysis. In this thesis, we further conduct a comprehensive study on computational analysis of sentiment from the multi-view data. The state-of-the-art approaches on single view (image or text) or multiview (image and text) data are introduced, and compared through extensive experiments conducted on our constructed dataset and other public datasets. More importantly, the effectiveness of the correlation between different views is also studied using the widely used fusion strategies and advanced multi-view feature extraction methods.Index Terms: Sentiment analysis, social media, multi-view data, textual feature, visual feature, joint feature learning.

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Low-pressure chemical vapor deposition (LPCVD) technique is utilized for SiN x passivation of AlGaN/GaN high-electron-mobility transistors (HEMTs). A robust SiN x / AlGaN interface featuring high thermal stability and well-ordered crystalline structure is achieved by a processing strategy of "passivation-prior-to-ohmic" in HEMTs fabrication. Effective suppression of surface-trap-induced current collapse and lateral interface leakage current are demonstrated in the LPCVD-SiN x passivated HEMTs, as compared with conventional plasma-enhanced chemical vapor deposition-SiN x passivated ones. Energy dispersive X-ray spectroscopy mapping analysis of SiN x /AlGaN interfaces suggests the interface traps are likely to stem from amorphous oxide/oxynitride interfacial layer. Index Terms-AlGaN/GaN high-electron-mobility transistors (HEMTs), current collapse, low-pressure chemical vapor deposition (LPCVD), oxidation, SiN x passivation.