基于微博用户的情绪变化分析
Chinese mood variation analysis based on Sina Weibo
查看参考文献23篇
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文摘
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通过网络大数据的方法宏观地分析微博用户在不同季节和时间的情绪变化.以195万微博活跃用户为样本,在每个季节下载一周的微博活跃用户数据,利用“中文心理分析系统”计算每个季节积极情绪和消极情绪词的词频.结果显示: 1)人们的综合情绪的2个高峰分别在中午和晚8点; 2)虽然人们在周末的积极情绪与工作日无异,但消极情绪在周末明显低于工作日; 3)人们在夏季的积极情绪和消极情绪最高,在秋季的积极情绪和消极情绪最低; 4)两种性别的情绪走势一致,但女性较男性有更多情绪表达、更情绪化,更易感. |
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其他语种文摘
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This study tried to recognize the Chinese mood variations in different seasons and time periods via the big data on Internet. Participants were 1.95 million active users of Sina Weibo. We downloaded their data of one week selected in each of the four seasons of one year. Then we used TextMind to calculate the ratios of positive affect (PA) and negative affect (NA) words. Results are given as follows. 1) Integrated mood had two peaks at noon and 8 o'clock in the evening. 2) Although PA in weekends was not different from that on the weekdays,NA in weekends was lower than that on weekdays. 3) In summer,both PA and NA were the highest. In autumn,both PA and NA were the lowest. 4) The two genders had similar mood variation trends,but females had more mood expressions than males and they were sentimental and susceptive. |
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来源
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中国科学院大学学报
,2016,33(6):815-824 【核心库】
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DOI
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10.7523/j.issn.2095-6134.2016.06.014
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关键词
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微博
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情绪
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生物节律
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大数据
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性别差异
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地址
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1.
中国科学院心理研究所, 中国科学院行为科学重点实验室, 北京, 100101
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北京大学人口研究所, 北京, 100871
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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2095-6134 |
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学科
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社会科学总论 |
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文献收藏号
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CSCD:5853521
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