Coronavirus (COVID-19) Global Sentiment Trend

This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple positive peaks and negative peaks that need to be further studied. The n-grams on those peaking and dropping periods can prove beneficial for better understanding the discourse.The dataset will be updated weekly and will continue until the development of the Coronavirus (COVID-19) Tweets Dataset is ongoing.What's inside the dataset files?Tweets collected every 10 minutes are sampled together, and an average sentiment score is computed. This dataset contains TXT files, each with two columns: (i) date/time and (ii) average sentiment. The first column is date/time and is by default in Unix timestamp (in ms). You can use this formula =cell/1000/60/60/24 + DATE(1970,1,1) in Spreadsheets, or this pd.to_datetime(dataframe_name[column],unit='ms') if you're comfortable with Python, to convert the Unix timestamp to human-readable format.

本数据集概览了Twitter平台上与新型冠状病毒肺炎（COVID-19）大流行相关的公共讨论整体情感趋势。该数据集的实时散点图可通过https://live.rlamsal.com.np处的「整体趋势」模块查看。该趋势图呈现出多个正向情感峰值与负向情感峰值，有待进一步研究。对这些峰值及波动阶段的n元语法（n-grams）进行分析，有助于更深入地理解相关讨论语境。本数据集将每周更新，持续至新型冠状病毒（COVID-19）相关Twitter推文数据集的开发工作进行期间。数据集文件包含哪些内容？每10分钟采集的推文将被合并采样，并计算平均情感得分。本数据集包含若干TXT格式文件，每个文件均包含两列数据：(i) 日期/时间，(ii) 平均情感得分。第一列为日期/时间信息，默认采用Unix毫秒时间戳格式。你可在电子表格软件中使用公式`=cell/1000/60/60/24 + DATE(1970,1,1)`将其转换为可读日期格式，若使用Python，则可通过`pd.to_datetime(dataframe_name[column],unit='ms')`方法完成转换。