4,422 research outputs found
Real Time Sentiment Change Detection of Twitter Data Streams
In the past few years, there has been a huge growth in Twitter sentiment
analysis having already provided a fair amount of research on sentiment
detection of public opinion among Twitter users. Given the fact that Twitter
messages are generated constantly with dizzying rates, a huge volume of
streaming data is created, thus there is an imperative need for accurate
methods for knowledge discovery and mining of this information. Although there
exists a plethora of twitter sentiment analysis methods in the recent
literature, the researchers have shifted to real-time sentiment identification
on twitter streaming data, as expected. A major challenge is to deal with the
Big Data challenges arising in Twitter streaming applications concerning both
Volume and Velocity. Under this perspective, in this paper, a methodological
approach based on open source tools is provided for real-time detection of
changes in sentiment that is ultra efficient with respect to both memory
consumption and computational cost. This is achieved by iteratively collecting
tweets in real time and discarding them immediately after their process. For
this purpose, we employ the Lexicon approach for sentiment characterizations,
while change detection is achieved through appropriate control charts that do
not require historical information. We believe that the proposed methodology
provides the trigger for a potential large-scale monitoring of threads in an
attempt to discover fake news spread or propaganda efforts in their early
stages. Our experimental real-time analysis based on a recent hashtag provides
evidence that the proposed approach can detect meaningful sentiment changes
across a hashtags lifetime
SUPER: Towards the Use of Social Sensors for Security Assessments and Proactive Management of Emergencies
Social media statistics during recent disasters (e.g. the 20 million tweets relating to 'Sandy' storm and the sharing of related photos in Instagram at a rate of 10/sec) suggest that the understanding and management of real-world events by civil protection and law enforcement agencies could benefit from the effective blending of social media information into their resilience processes. In this paper, we argue that despite the widespread use of social media in various domains (e.g. marketing/branding/finance), there is still no easy, standardized and effective way to leverage different social media streams -- also referred to as social sensors -- in security/emergency management applications. We also describe the EU FP7 project SUPER (Social sensors for secUrity assessments and Proactive EmeRgencies management), started in 2014, which aims to tackle this technology gap
Social media analytics: a survey of techniques, tools and platforms
This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing
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