Semantic patterns for sentiment analysis of Twitter

Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

Coding together at scale:GitHub as a collaborative social network

GitHub is the most popular repository for open source code (Finley 2011). It has more than 3.5 million users, as the company declared in April 2013, and more than 10 million repositories, as of December 2013. It has a publicly accessible API and, since March 2012, it also publishes a stream of all the events occurring on public projects. Interactions among GitHub users are of a complex nature and take place in different forms. Developers create and fork repositories, push code, approve code pushed by others, bookmark their favorite projects and follow other developers to keep track of their activities. In this paper we present a characterization of GitHub, as both a social network and a collaborative platform. To the best of our knowledge, this is the first quantitative study about the interactions happening on GitHub. We analyze the logs from the service over 18 months (between March 11, 2012 and September 11, 2013), describing 183.54 million events and we obtain information about 2.19 million users and 5.68 million repositories, both growing linearly in time. We show that the distributions of the number of contributors per project, watchers per project and followers per user show a power-law-like shape. We analyze social ties and repository-mediated collaboration patterns, and we observe a remarkably low level of reciprocity of the social connections. We also measure the activity of each user in terms of authored events and we observe that very active users do not necessarily have a large number of followers. Finally, we provide a geographic characterization of the centers of activity and we investigate how distance influences collaboration.

Fe-doped and -mediated graphitic carbon nitride nanosheets for enhanced photocatalytic performance under natural sunlight

Herein, we demonstrate the synthesis of highly efficient Fe-doped graphitic carbon nitride (g-C3N4) nanosheets via a facile and cost effective method. The synthesized Fe-doped g-C3N4 nanosheets were well characterized by various analytical techniques. The results revealed that the Fe exists mainly in the +3 oxidation state in the Fe-doped g-C3N4 nanosheets. Fe doping of g-C3N4 nanosheets has a great influence on the electronic and optical properties. The diffuse reflectance spectra of Fe-doped g-C3N4 nanosheets exhibit red shift and increased absorption in the visible light range, which is highly beneficial for absorbing the visible light in the solar spectrum. More significantly, the Fe-doped g-C3N4 nanosheets exhibit greatly enhanced photocatalytic activity for the degradation of Rhodamine B under sunlight irradiation. The photocatalytic activity of 2 mol% Fe-doped g-C3N4 nanosheets is almost 7 times higher than that of bulk g-C3N4 and 4.5 times higher than that of pure g-C3N4 nanosheets. A proposed mechanism for the enhanced photocatalytic activity of Fe-doped g-C3N4 nanosheets was investigated by trapping experiments. The synthesized photocatalysts are highly stable even after five successive experimental runs. The enhanced photocatalytic performance of Fe-doped g-C3N4 nanosheets is due to high visible light response, large surface area, high charge separation and charge transfer. Therefore, the Fe-doped g-C3N4 photocatalyst is a promising candidate for energy conversion and environmental remediation.