EAGER: Using Social Media and Crowdsourcing to Create a New Affect Dictionary

文本和口语中的情感和情感研究在很大程度上依赖于人类对语料库的标注和人类对上下文中单词的判断，为预测提供黄金标准数据和有用的特征。目前的资源，如Whissell的语言情感词典(DAL)，由于覆盖范围有限，其实用价值有限。为了提供更丰富的黄金标准，近年来，计算语言学家一直在利用Amazon Machine Turk等众包网站来完成以前由训练有素的注释员或人类受试者在实验室实验中完成的注解任务。最近，已经有一些研究将社交媒体网站用于类似的目的(挖掘现有的社交交流以获取信息，并创建游戏来获取新的信息)。在这个项目中，PI将探索使用所有这三种方法来增强关于人类对词汇内容的情感判断的现有数据，以支持她正在进行的对情感文本和言语的分类的研究(其主要目标是通过考虑情感文本的更细微的方面，超越依赖于声学和韵律特征的简单的配价判断)。她将考察大众外包、社交媒体挖掘和社交网站中实施的游戏的价值，以确定在获得可靠的情感内容注释和判断方面可以学到什么教训。广泛影响：这项探索性研究有可能开辟关于词汇项情感内涵的新信息来源，这对研究文本和言语情感的研究人员将是无价的。在文本和语音中自动识别情感的进展将在商业和医学等领域有重大应用。评估消费者意见的商业兴趣正迅速从焦点小组转移到产品评论分析，而医疗信息学研究人员也同样试图通过挖掘在线论坛来了解患者对诊断、药物和治疗的态度。这种努力的一个主要组成部分是使用情感词典。新的和更好的词汇情感内涵来源应该证明对这些努力有极大的帮助，提高我们从个人在线免费提供的数据中学习有用、实用信息的能力。通过这些实验获得的经验教训和后续数据收集的结果将以新的情感词典的形式提供给更大的研究界。

Research on sentiment and emotion in textual and spoken language relies heavily on human annotation of corpora and human judgments of words in context to provide gold standard data and useful features for prediction. Current resources such as Whissell's Dictionary of Affect in Language (DAL) are of limited utility, due to their limited coverage. To provide richer gold standards, in recent years computational linguists have been making use se of crowd-sourcing websites such as Amazon Mechanical Turk to accomplish annotation tasks that previously would have been done by trained annotators or human subjects in laboratory experiments. Recently there has been some research on using social media websites for similar purposes (mining existing social exchanges for information and creating games to elicit new information). In this project the PI will explore the use of all three of these approaches to augment existing data on human judgments of the emotional content of lexical items to support her ongoing investigation into the classification of emotional text and speech (a major objective of which is to move beyond simple valence judgments relying upon acoustic and prosodic features by taking into account more nuanced aspects of affective text). She will examine the value of crowd-sourcing, social media mining and games implemented in social websites to ascertain what lessons can be learned about acquiring reliable annotations and judgments of emotional content.Broader Impacts: This exploratory research has the potential to open up new sources of information about the affective connotations of lexical items that will be invaluable to researchers working in text and in speech affect. Advances in the automatic identification of affect in text and in speech would have major applications in fields such as business and medicine, inter alia. Business interests in assessing consumer opinion is rapidly moving from focus groups to analyses of product reviews, while medical informatics researchers similarly try to learn patient attitudes to diagnoses, drugs, and treatments by mining online forums. A major component of such endeavors is the use of affect dictionaries. New and better sources of affective connotations of lexical items should prove enormously helpful in these efforts, increasing our ability to learn useful, practical information from the data individuals provide freely online. The lessons learned through these experiments and results of subsequent data collection will be made available to the larger research community in the form of new affect lexicons.