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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (4): 15-26    DOI: 10.11925/infotech.2096-3467.2019.0500
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Analyzing Public Sentiments from the Perspective of City Profiles
Ye Guanghui(),Zeng Jieyan,Hu Jinglan,Bi Chongwu
School of Information Management, Central China Normal University, Wuhan 430079, China
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Abstract  

[Objective] This study constructs an evolution model for social sentiment analysis from the perspective of city profiles, aiming to grasp city dynamics, guide public opinions, as well as identify and predict potential issues. [Methods] We firstly used the LDA2Vec algorithm to extract city themes from each time window. Then, we applied a dictionary-based sentiment analysis method to fine-grain the emotion categories of city themes, and calculated their emotional intensities. Finally, we tracked city events arising changes of public sentiments with the TF-IDF algorithm, and built the ARMA model to predict social sentiment trends. [Results] Our model’s accuracy rate for predicting emotional intensity of “like” reached 97%, while those of the “dislike” scores were up to 90%. [Limitations] We did not include unexpected events as an influencing factor to the proposed model. [Conclusions] Our method could effectively identify city events and predict emotional changes of public opinions.

Key wordsCity      Profile      Emotional      Evolution      LDA2Vec      Public      Opinion      Monitoring     
Received: 12 May 2019      Published: 01 June 2020
ZTFLH:  TP393  
Corresponding Authors: Ye Guanghui     E-mail: 3879-4081@163.com

Cite this article:

Ye Guanghui,Zeng Jieyan,Hu Jinglan,Bi Chongwu. Analyzing Public Sentiments from the Perspective of City Profiles. Data Analysis and Knowledge Discovery, 2020, 4(4): 15-26.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0500     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I4/15

Analysis Process of Public Emotion Evolution
初始情感类别 否定词修饰后的情感类别
a×乐
b×好
Emotion Conversion Modified by Negative Word
强度 程度副词 个数
2.0 百分之百、倍加、备至、非常、极度、极端…… 38
1.8 越、越发、愈发、愈加、过度、过分、过火…… 29
1.5 不过、不少、不胜、多加、多么、分外、格外…… 13
1.2 大不了、多、更、更加、更进一步、更为、还要…… 12
0.8 略、略加、略略、略微、略为、蛮、稍、稍稍…… 15
0.5 半点、不大、不丁点儿、不甚、不怎么、丝毫…… 12
Degree Adverb List
时间 主题编号 主题 主题特征词(部分)
2014.01-2014.06 Topic1-1 城市文化 热干面 黄鹤楼 大学生 热情 耿直
Topic1-2 城市交通 司机 公交车 急 拥堵 混乱
Topic1-3 城市发展 科教 满城挖 城乡结合部 经济 物价
Topic1-4 城市环境 气候 冬冷夏热 天气 热 火炉
2014.07-2014.12 Topic2-1 城市文化 热干面 武汉大学 樱花 美食 VOX
Topic2-2 城市交通 司机 剽悍 暴躁 公交车 堵
Topic2-3 城市发展 满城挖 人多 学校 生活 服务
Topic2-4 城市环境 热 火炉 环境 烟尘 脏乱差
2015.01-2015.06 Topic3-1 城市文化 热干面 小龙虾 武汉大学 东湖 朋克
Topic3-2 城市交通 司机 出租车 拒载 交通 堵
Topic3-3 城市发展 人才 留不住 工资水平 生活气息 汉口
Topic3-4 城市环境 天气 夏天 冬天 脏乱 拥挤
2015.07-2015.12 Topic4-1 城市文化 黄鹤楼 小龙虾 湖北省博物馆 武汉大学
Topic4-2 城市交通 公交车 地铁 光谷 拥堵 堵
Topic4-3 城市发展 满城挖 修路 人文 发展 很大
Topic4-4 城市环境 天气 热 看海 环境 不好
2016.01-2016.06 Topic5-1 城市文化 樱花 昙华林 东湖 周黑鸭 鸭脖
Topic5-2 城市交通 公交车 出租车 堵 交通 拥挤
Topic5-3 城市发展 房价 工资 年轻人 大学生 满城挖
Topic5-4 城市环境 夏天 热 空气 雾霾 看海
2016.07-2016.12 Topic6-1 城市文化 东湖 长江大桥 风景 轮渡 莲藕
Topic6-2 城市交通 司机 暴躁 九省通衢 堵车 严重
Topic6-3 城市发展 建设 变化 道路 经济 改善
Topic6-4 城市环境 冬冷夏热 霾 灰尘 空气 糟糕
2017.01-2017.06 Topic7-1 城市文化 鸭脖 热干面 东湖 武汉大学 码头文化
Topic7-2 城市交通 交通 拥堵 便利 公交车 开车
Topic7-3 城市发展 市井气息 人多 外地人 就业 商业
Topic7-4 城市环境 夏天 热 冬天 暴雨 潮湿
2017.07-2017.12 Topic8-1 城市文化 长江大桥 夜景 建筑 黄鹤楼 热干面
Topic8-2 城市交通 司机 脾气 光谷 拥堵 混乱
Topic8-3 城市发展 满城挖 每天 不一样 房价 工资
Topic8-4 城市环境 夏天 雨季 热 天气 灰蒙蒙
2018.01-2018.06 Topic9-1 城市文化 过早 热干面 豆皮 人情味 生活气息
Topic9-2 城市交通 公交车 超速 凶猛 过山车 晕车
Topic9-3 城市发展 道路 施工 建设 发展 迅速
Topic9-4 城市环境 冬天 夏天 气候 恶劣 变化
2018.07-2018.12 Topic10-1 城市文化 过早 热干面 豆皮 市井 历史
Topic10-2 城市交通 出租车 过山车 超速 晕车 拥堵
Topic10-3 城市发展 修路 便利 教育 军运会 宜居
Topic10-4 城市环境 气候 冬冷夏热 空气 差 湿气
Themes Distribution of City Profiles
Emotion Type and Intensity Corresponding to Different Topics in Each Time Window
Theme Emotional Evolution Curve
主题编号 情感极性
临界点类型
关键词
Topic5-2 好→恶 司机 绕路 堵车 严重 光谷 不礼让
Topic6-2 恶→好 公交 地铁 便宜 交通 枢纽 便利
Topic3-4 好→恶 冬冷 夏热 雾霾 四季 随机 播放
Topic4-4 恶→好 天气 阴晴 不定 脏乱 街道 改善
Topic5-4 好→恶 气候 多变 很热 火炉 雾霾 脏话
Topic6-4 恶→好 风景 落雁岛 环境 美 大江 大湖
Topic8-4 好→恶 雨 多 灰尘 湿热 看海 石楠花
Text Keywords at Turning Point
序列名称 估计方程 预测值 实际值
“好”情感值时间序列 Like(t)=α+εt 1 841 1 897
“恶”情感值时间序列 Unlike(t)=α+εt 872 972
Modeling and Prediction of Sentiment Time Series
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