😭The Saddest Day on Twitter: Sentiment Analysis & Engagement Trends in Company’s Tweets

😭The Saddest Day on Twitter: Sentiment Analysis & Engagement Trends in Company’s Tweets

The Saddest Day on Twitter: Sentiment Analysis & Engagement Trends in Company's Tweets. Data analysis & visualisation of weekly trends in engagement metrics and sentiment values in tweets from 6 months.

If a tweet is sent and no one is around to check analytics, does it make an impact? Ok, this is a _rather loose _paraphrase of the question about a tree falling in the woods. 🌳 But, you know what I’m getting at.

I’ve been tweeting as JAM (Twitter makingjam) for more than a year. The time has come to see in detail what insights could be drawn from this exercise in copywriting and communication. Apart from mentions, hashtagsemoji, and text, I was interested to see how our tweets perform depending on the day of the week.

Because why bother tweeting on Sundays if everyone is on a phone-free brunch?🤷‍♀️

Time for datetime

Here is how the first few rows of the data frame look like before we started.

Image for post

40 columns of various degrees of usefulness…

_We had _529 rows and 40 columns, but in an initial stage of the analysis we removed some of them (you can see the full data analysis in this notebook).

The first step is to handle our favourite variable which is datetime! Anyone else still confuses strftime with strptime? 🙋‍♀️ It’s like confusing the left and the right side, maybe we’ll never cure ourselves of it.

We’ll split the column into date and hour and extract the name of the day of the week.

data-visualization data-analysis python data sentiment-analysis

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