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语料库的人工注释和上下文中的词语判断，以提供用于预测的金标准数据和有用特征。 目前的资源，如惠塞尔的语言情感词典（DAL）是有限的效用，由于其有限的覆盖面。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亚马逊土耳其机器人等众包网站来完成注释任务，而这些任务以前是由经过训练的注释者或实验室实验中的人类受试者完成的。 最近有一些关于将社交媒体网站用于类似目的的研究（挖掘现有的社交交流信息，并创建游戏以获取新信息）。 在这个项目中，PI将探索使用所有这三种方法来增加现有的数据，人类判断的词汇的情感内容，以支持她正在进行的调查到情感文本和语音的分类（其中一个主要目标是超越简单的效价判断依赖于声学和韵律特征，考虑到情感文本的更细微的方面）。 她将研究众包的价值，社会媒体挖掘和游戏中实施的社会网站，以确定什么教训可以学到关于获得可靠的注释和判断的情感内容。更广泛的影响：这种探索性的研究有可能开辟新的信息来源的情感内涵的词汇，这将是非常宝贵的研究人员在文本和语音影响。 在文本和语音中自动识别情感方面取得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利益正在迅速从焦点小组转移到产品评论分析，而医学信息学研究人员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的来源应该证明在这些努力中有很大的帮助，提高我们从个人在线免费提供的数据中学习有用的实用信息的能力。 通过这些实验和后续数据收集的结果所获得的经验教训将以新的情感词汇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.