covidtwitterdataset
印度新冠疫情相关推文数据集，基于15个关键词收集六个城市2020年2至7月推文。
基本信息
资源简介
该数据集收集了印度新冠疫情期间与特定问题相关的推文，通过分析Google趋势中的热门关键词，选取了15个关键词进行推文收集。数据集涵盖了印度六个主要城市的推文，时间范围为2020年2月1日至7月31日。
下载信息
注册下载
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使用方式
数据集获取
git clone https://github.com/Vivek0712/covidtwitterdataset.git
curl -L -o repo.zip https://github.com/Vivek0712/covidtwitterdataset/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
covidtwitterdataset
Data Collection
Keywords
To collect a variety of tweets addressing some typical issues faced during the Covid-19 lockdown pandemic in India, the trending keywords based on Google Trends were analyzed and collected. The top 15 keywords selected are ‘‘corona’’,‘‘virus’’,‘‘coronavirus’’, ‘‘covid19’’, ‘‘social’’, ‘‘job’’ , ‘‘loss’’ , ‘‘jobloss’’, ‘‘migrant’’ , ‘‘treatment’’ , ‘‘hospital’’, ‘‘health’’, ‘‘mask’’, ‘‘lockdown’’, ‘‘curfew’’.
Location
In order to express the sentiment of the whole country, it is mandatory to collect tweets from different location within the country. The tweets were collected with geotagged locations of six major cities in India, i.e, Delhi, Mumbai, Kolkata, Chennai, Bengaluru, Hyderabad. The first three cities represent North India and the latter three cities represent South India. The radius of area for each city was set to 100km.
Date Range
The tweets were collected from February 1 to July 31, 2020.
Data Preprocessing
• Lower casing
• Removal of Punctuations
• Removal of Stopwords
• Removal of Frequent words
• Removal of Rare words
• Stemming
• Lemmatization
• Conversion of emoticons to words
• Conversion of emojis to words
• Removal of URLs
• Removal of HTML tags
• Chat words conversion
• Spelling correction
• Removal of ‘#’ from hashtags
• Addition of hashtags to tweet
数据加载示例（表格/文本类）
import pandas as pd, glob, os
files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
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