EMOTIVE - Extracting the Meaning Of Terse Information in a geo-Visualisation of Emotion

EMOTIVE - 在情感的地理可视化中提取简洁信息的含义

• 批准号: EP/J020532/1
• 负责人: Thomas Jackson
• 金额: $ 11.28万
• 依托单位: Loughborough University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ020532%2F1
• 关键词: EMOTIVE; Extracting; Meaning; Terse; Information

The ability for ordinary people to express and exchange their opinions and feelings has increased beyond all expectations in the past ten years of internet expansion and availability. To the military and national security agencies this has provided both opportunities and challenges. Opportunities have emerged in the sense of readily available awareness of discontent and oppositional movements and initiatives. Recent urban disturbances have illustrated the key role played by social networks in the fast-moving events of Summer 2011. The challenges have escalated due to the sheer number of sources of social interaction and public communication media. This research addresses some of these issues in a bold initiative to combine well established and considered science with the increasingly familiar tools of Web 2.0.Four of the most popular sources of the public exchange of ideas (email, social networks, such as Facebook, microblogs, such as Twitter and comments to newspaper editorials and high-profile stories) will be selectively monitored. Sensitive words and phrases which may be of concern to the military and national security agencies will be extracted by extending a Natural Language Processing technique already developed for email by the Principal Investigator. The team will develop an ontology (a rule-based linguistic database) in which the extracted words and phrases will be semantically filtered and restricted to a manageable set of agreed terms. An example of how the ontology will work can be illustrated by suggesting the number of ways the word 'looting' might be expressed in, for example, established vocabulary (raiding, pillaging, ransacking, etc.) as well as in urban and regional street language and text speak ( doin' over, scamming, etc.). The ontology will be trained to recognise the words and phrases, make semantic links between them and deliver one or more accepted descriptors to the analysts. EMOTIVE will monitor the traffic of sensitive words and phrases filtered through the ontology when applied to specific incidents, individuals and groups. Increased activity will be indicated by frequency of occurrence or severity, which will be presented through a concept cloud which uses the size of words as a metaphor for frequency and hence importance.Further to this, a second ontology will be created in which words and phrases that express emotion will be harnessed and this ontology will process the emotionally charged words and phrases extracted from the four sources described above in a similar way to the firstThe output of both ontologies will be linked, so that the monitoring analyst will be presented with a colour-coded indication of the strength of emotion attached to the language-based terms.The final feature in Emotive will be a geo interface to point to the location of the emotionally charged traffic. This interface will be refreshed every 60 seconds with the effect of helping to identify sensitive hot spots of communication and activities. Outputs from the system consisting of effectively presented new knowledge will enable defence and national security agencies both to predict and monitor selected events as they develop and will assist in the formulation of policy.It can be argued that the general public will be direct beneficiaries of this research in that the defence and national security agencies who act as guardians of public safety and order will be further equipped by this tool to identify, evaluate and ultimately safeguard the public from potentially harmful events.Defence and national agencies will already be experienced at monitoring these data sources but this tool adds an extra filter of analysis, it will work in almost real time, will amalgamate data from several sources if desired and will provide harmonised output.

在过去十年互联网的发展和普及中，普通人表达和交换意见和感受的能力已经超出了所有人的预期。对军队和国家安全机构来说，这既提供了机会，也提出了挑战。机会已经出现，因为人们随时可以意识到不满情绪和反对运动和倡议。最近的城市骚乱表明了社交网络在2011年夏季快速发展的事件中发挥的关键作用。由于社会互动和公共传播媒体的来源数量庞大，这些挑战已经升级。这项研究以一种大胆的举措解决了其中的一些问题，将联合收割机成熟的科学与日益熟悉的Web 2.0工具相结合。四种最流行的公共思想交流来源（电子邮件、社交网络（如Facebook）、微博（如Twitter）以及对报纸社论和高调报道的评论）将被有选择地监测。军事和国家安全机构可能关注的敏感单词和短语将通过扩展主要研究员已经为电子邮件开发的自然语言处理技术来提取。该小组将开发一个本体（一个基于规则的语言数据库），其中提取的单词和短语将被语义过滤，并限制为一组可管理的商定术语。本体如何工作的一个例子可以通过建议单词“抢劫”可能被表达的方式的数量来说明，例如，建立词汇表（raiding，pillaging，ransacking等）。以及在城市和地区的街头语言和文本说话（做'过，诈骗等）。本体将被训练来识别单词和短语，在它们之间建立语义链接，并向分析师提供一个或多个可接受的描述符。EMOTIVE将监控通过本体过滤的敏感单词和短语的流量，当应用于特定事件，个人和团体时。增加的活动将通过发生频率或严重程度来指示，这将通过使用单词大小作为频率和重要性的隐喻的概念云来呈现。第二本体将被创建，其中表达情感的单词和短语将被利用，并且该本体将处理从上述四个源中提取的充满情感的单词和短语，与第一个相似的方式。两个本体的输出将被链接，以便监控分析师将被呈现与基于语言的术语相关联的情感强度的颜色编码指示。Emotive中的最后一个特征将是地理界面，以指向情感化流量的位置。该界面将每60秒刷新一次，以帮助识别通信和活动的敏感热点。该系统的产出包括有效提供的新知识，将使国防和国家安全机构能够预测和监测发展中的选定事件，并协助制定政策，可以说，公众将是这项研究的直接受益者，因为作为公共安全和秩序监护人的国防和国家安全机构将进一步配备这一工具国防和国家机构在监测这些数据源方面已经很有经验，但这一工具增加了一个额外的分析过滤器，它将在几乎真实的时间内工作，如果需要，将合并来自几个来源的数据，并将提供协调一致的输出。

项目成果

The Role of Visualisations in Social Media Monitoring Systems

可视化在社交媒体监控系统中的作用

• 发表时间: 2015
• 作者: Sykora, M

Emotive ontology: extracting fine-grained emotions from terse, informal messages.

情感本体论：从简洁、非正式的消息中提取细粒度的情感。

• 作者: Martin Sykora (Co-Author)

Real-Time Detection, Tracking, and Monitoring of Automatically Discovered Events in Social Media

• DOI: 10.3115/v1/p14-5007
• 发表时间: 2014
• 作者: M. Osborne;S. Moran;R. McCreadie;A. V. Lünen;M. Sykora;A. Cano;N. Ireson;Craig Macdonald;I. Ounis;Yulan He;Tom Jackson;F. Ciravegna;A. O'Brien

Using Social Media to Analyse the Scottish Referendum Journey

利用社交媒体分析苏格兰公投之旅

• 发表时间: 2015
• 作者: Jackson, TW

National Security and Social Media Monitoring: A Presentation of the EMOTIVE and Related Systems

• DOI: 10.1109/eisic.2013.38
• 发表时间: 2013-08
• 期刊: 2013 European Intelligence and Security Informatics Conference
• 作者: M. Sykora;Thomas W. Jackson;A. O'Brien;Suzanne Elayan