A large-scale sentiment analysis for Yahoo! answers

Sentiment extraction from online web documents has re-cently been an active research topic due to its potential use in commercial applications. By sentiment analysis, we refer to the problem of assigning a quantitative positive/negative mood to a short bit of text. Most studies in this area are limited to the identification of sentiments and do not inves-tigate the interplay between sentiments and other factors. In this work, we use a sentiment extraction tool to investi-gate the influence of factors such as gender, age, education level, the topic at hand, or even the time of the day on sen-timents in the context of a large online question answering site. We start our analysis by looking at direct correlations, e.g., we observe more positive sentiments on weekends, very neutral ones in the Science & Mathematics topic, a trend for younger people to express stronger sentiments, or people in military bases to ask the most neutral questions. We then extend this basic analysis by investigating how properties of the (asker, answerer) pair affect the sentiment present in the answer. Among other things, we observe a dependence on the pairing of some inferred attributes estimated by a user’s ZIP code. We also show that the best answers differ in their sentiments from other answers, e.g., in the Business & Finance topic, best answers tend to have a more neutral sentiment than other answers. Finally, we report results for the task of predicting the attitude that a question will provoke in answers. We believe that understanding factors influencing the mood of users is not only interesting from a sociological point of view, but also has applications in ad-vertising, recommendation, and search

Video Copy Detection Using Multiple Visual Cues and MPEG-7 Descriptors

We propose a video copy detection framework that detects copy segments by fusing the results of three different techniques: facial shot matching, activity subsequence matching, and non-facial shot matching using low-level features. In facial shot matching part, a high-level face detector identifies facial frames/shots in a video clip. Matching faces with extended body regions gives the flexibility to discriminate the same person (e.g., an anchor man or a political leader) in different events or scenes. In activity subsequence matching part, a spatio-temporal sequence matching technique is employed to match video clips/segments that are similar in terms of activity. Lastly, the non-facial shots are matched using low-level MPEG-7 descriptors and dynamic-weighted feature similarity calculation. The proposed framework is tested on the query and reference dataset of CBCD task of TRECVID 2008. Our results are compared with the results of top-8 most successful techniques submitted to this task. Promising results are obtained in terms of both effectiveness and efficienc

Hierarchical organization of urban mobility and its connection with city livability

The recent trend of rapid urbanization makes it imperative to understand urban character-istics such as infrastructure, population distribution, jobs, and services that play a key role inurban livability and sustainability. A healthy debate exists on what constitutes optimalstructure regarding livability in cities, interpolating, for instance, between mono- and poly-centric organization. Here anonymous and aggregatedflows generated from three hundredmillion users, opted-in to Location History, are used to extract global Intra-urban trips. Wedevelop a metric that allows us to classify cities and to establish a connection betweenmobility organization and key urban indicators. We demonstrate that cities with stronghierarchical mobility structure display an extensive use of public transport, higher levels ofwalkability, lower pollutant emissions per capita and better health indicators. Our frameworkoutperforms previous metrics, is highly scalable and can be deployed with little cost, even inareas without resources for traditional data collection.A.B. is funded by the Conselleria d’Educacio, Cultura i Universitats of the Government of the Balearic Islands and the European Social Fund. A.B. and J.J.R. also acknowledge partial funding from the Spanish Ministry of Science, Innovation and Universities, the National Agency for Research Funding AEI and FEDER (EU) under the grant PACSS (RTI2018-093732-B-C22) and the Maria de Maeztu program for Units of Excellence in R&D (MDM-2017-0711). G.G. and S.H. acknowledge funding from the Department of Economic Development (DED), New York through the NYS Center of Excellence in Data Science at the University of Rochester (C160189). G.G. and H.B. also acknowledge support in part by the U. S. Army Research Office (ARO) under grant number W911NF-18-1-0421

Reply to: On the difficulty of achieving differential privacy in practice: user-level guarantees in aggregate location data

none14openAleix Bassolas; Hugo Barbosa-Filho; Brian Dickinson; Xerxes Dotiwalla; Paul Eastham; Riccardo Gallotti; Gourab Ghoshal; Bryant Gipson; Surendra A. Hazarie; Henry Kautz; Onur Kucuktunc; Allison Lieber; Adam Sadilek; Jose J. RamascoBassolas, Aleix; Barbosa-Filho, Hugo; Dickinson, Brian; Dotiwalla, Xerxes; Eastham, Paul; Gallotti, Riccardo; Ghoshal, Gourab; Gipson, Bryant; Hazarie, Surendra A.; Kautz, Henry; Kucuktunc, Onur; Lieber, Allison; Sadilek, Adam; Ramasco, Jose J