The report has been added with textual analysis of Twitter data. Using the TextBlob library in Python tweets can be analysed for sentiment (positive or negative). This has resulted in two new metrics;
- Average polarity per hour – Tweets are analysed on polarity (-1 to +1, negative to positive). This results in a line chart that shows average sentiment per hour
- Count of positive and negative sentiment – Tweets are analysed on outspoken negative or positive tweets (<-0,5 or >0,5). This result in a area graph which shows the volume of negative tweets versus positive Tweets.
Historic data has to be build up over time, but most recent data has been added in the data section. Latest reports including the new metrics has also been uploaded. Subscribe to the newsletter to receive new reports first!