Scalable sentiment analytics

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Employee Analytics through Sentiment Analysis

People discuss and talk about the most diverse topics in social media platforms, including about their jobs. This results in a stream of employeerelated data, and organizations are increasingly interested in making sense of this data on an ongoing basis in order to assess key factors such as employee engagement, retention and satisfaction. In this paper we propose to estimate such factors from ...

متن کامل

Large Scale Aggregated Sentiment Analytics

In the past years we have witnessed Sentiment Analytics becoming increasingly popular topic in Information Retrieval, which has established itself as a promising direction of research. With the rapid growth of the user-generated content represented in blogs, forums, social networks and micro-blogs, it became a useful tool for social studies, market analysis and reputation management, since it m...

متن کامل

Scalable I/O and analytics

High-performance computing systems have already approached peta-scale with hundreds of thousands of processors/cores in many deployments. These systems promise a new level of predictive and knowledge discovery ability as researchers gain the capability to model dependencies between phenomena at scales not seen earlier. These applications are highly I/O and data intensive, leading scientists to ...

متن کامل

Squall: Scalable Real-time Analytics

Squall is a scalable online query engine that runs complex analytics in a cluster using skew-resilient, adaptive operators. Squall builds on state-of-the-art partitioning schemes and local algorithms, including some of our own. This paper presents the overview of Squall, including some novel join operators. The paper also presents lessons learned over the five years of working on this system, a...

متن کامل

Scalable graph analytics with GRADOOP

Many Big Data applications in business and science require the management and analysis of huge amounts of graph data. Previous approaches for graph analytics such as graph databases and parallel graph processing systems (e.g., Pregel) either lack sufficient scalability or flexibility and expressiveness. We are therefore developing a new end-to-end approach for graph data management and analysis...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES

سال: 2016

ISSN: 1300-0632,1303-6203

DOI: 10.3906/elk-1311-128