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使用心理语言和众包词典挖掘匿名社交媒体帖子
AnonyMine: Mining anonymous social media posts using psycho-lingual and crowd-sourced dictionaries |
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| 课程网址: |
https://videolectures.net/videos/kdd2016_paul_anonymous_social
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| 主讲教师: |
Arindam Paul
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| 开课单位: |
KDD 2016研讨会
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| 开课时间: |
2025-02-04
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| 课程语种: |
英语
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| 中文简介: |
在观点挖掘和情感分析领域有很多研究活动，涉及文本中观点、情感和主观性的计算处理。社交媒体网站因讨论令人不舒服的话题而变得越来越受欢迎。然而，用于挖掘和自动标记讨论自我披露的帖子的资源有限。对于一个可用于监测用户情绪状态的系统，无论是对研究界还是心理健康和商业目的，都有很大的激励作用本文介绍了一个案例，我们利用心理语言学和众包词典中的信息创建了一个系统，该系统可以自动预测社交媒体网站（Facebook Confessions）上关于禁忌话题的匿名帖子。我们对最受欢迎的禁忌话题的准确率超过80%，在所有禁忌类别中的总体准确率为61.25%。我们通过两种方式评估我们的系统：a）与另一个匿名社交媒体平台YikYak上的人类注释帖子进行比较；b）与现有的最先进模型进行评估。
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| 课程简介: |
There is lot of research activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text. Social media websites have become increasingly popular for discussing uncomfortable topics. However, there are limited resources for mining and automatically labeling posts discussing self-disclosure. There is great incentive for a system which can be useful for monitoring emotional state of users, both for the research community as well as for mental health and business purposes.\\ This paper presents a case where we leverage information from psycho-lingual and crowd-sourced dictionaries to create a system which can automatically predict anonymous posts about taboo topics on a social media site (Facebook Confessions). We achieve more than 80% accuracy for the most popular taboo topics, and an overall accuracy of 61.25 % across all taboo categories. We evaluate our system in two ways: a) comparing against human-annotated posts on another anonymous social media platform YikYak b) an evaluation against existing state-of-the-art models.
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| 关 键 词: |
AnonyMine; 心理语言; 众包词典; 挖掘帖子
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| 课程来源: |
视频讲座网
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| 数据采集: |
2025-03-11：liyq
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| 最后编审: |
2025-03-11：liyq
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| 阅读次数: |
6
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