情绪分析
计算机科学
跟踪(教育)
可用性
离群值
社会化媒体
政府(语言学)
任务(项目管理)
时间序列
微博
人工智能
数据科学
机器学习
万维网
心理学
人机交互
工程类
哲学
系统工程
语言学
教育学
作者
Anastasia Giachanou,Fábio Crestani
标识
DOI:10.1145/2911451.2914702
摘要
In recent years social media have emerged as popular platforms for people to share their thoughts and opinions on all kind of topics. Tracking opinion over time is a powerful tool that can be used for sentiment prediction or to detect the possible reasons of a sentiment change. Understanding topic and sentiment evolution allows enterprises or government to capture negative sentiment and act promptly. In this study, we explore conventional time series analysis methods and their applicability on topic and sentiment trend analysis. We use data collected from Twitter that span over nine months. Finally, we study the usability of outliers detection and different measures such as sentiment velocity and acceleration on the task of sentiment tracking.
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