Hello world! Welcome to report where I'm using machine learning to analyze tweets about specified topic and present results in form of various and easy to understand charts. The sentiment analysis algorithm has been developed as part of my Master Thesis in 2017/2018.

This report is currently being published exclusively here on Steemit.

Parameters

Today's analysis has been executed on tweets which contain word "universal basic income" and were published between 2011-01-01 and 2018-12-31. Detailed specification of the data is shown in the following list:

• Keyword: universal basic income
• From: 2011-01-01
• To: 2018-12-31
• Number of analyzed tweets: 40000
• Tweets per week: 95
• Language: en
• Geographical location: Not specified

Results

Sentiment

After downloading 40000 tweets between the specified dates, sentiment analysis has been executed on each and every one of those tweets. Sentiment score has been then aggregated over weeks and months, to lower the granularity of results on the time axis and then plotted as a following linechart.

^{Sentiment of tweets for keyword "universal basic income"}

My subjective comment on the chart: Hmm this is pretty interesting. While the overal sentiment doesn't seem to change that much, it looks like it's getting more and more smooth. In the first years, we can see bigger extremes on both sides - postive as well as negative but in the last 2-3 years. there are almost no extremes. I believe this is the result of the topic being more and more discussed and better and better explained.

Aggregation using heatmaps

To show the general trend/pattern in the sentiment, linechart works great. We can see the bigger timeframe and estimate the long-term direction. But if you're interested in particular month or week, it's hard and in case of weeks actually impossible to see the change. Has an athlete put the great performance in particular match? Has the brand/company released a new line of product? So see such low lever changes, following 2 heatmaps are to be used.

^{Chart shows average sentiment per month where 0.36 is the worst and 0.92 the best achieved score}

My subjective comment on the chart:I'm not sure how to interpret this chart so if you guys see some explanation/pattern in there, please let me know in the comments. I'd reply to every comment (as I always do anyway)

Most frequently used words

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Another very interesting aspect to look into are the repeatedly used words using wordclouds. Even more interesting is to compare two wordclouds generated from different time - usually before and after some event/change happened. If you give this a second though, the problem here is that many short words (like "and", "or", "with" and so on) are used almost in every sentence and would also show up in wordclouds. To mitigate this, I've removed list of 153 so called stopwords. Additionally I've also removed words typical for this area listed in the end of the report*.

^{Most often used words in tweets containing word "universal basic income" before and after 2015-12-31.}

My subjective comment on the chart:I'm not sure how to interpret this chart so if you guys see some explanation/pattern in there, please let me know in the comments. I'd reply to every comment (as I always do anyway). We can see that the word "will" is getting more and more popular and also "need" and "people" got a big boost. Also the word robot got substituted by Silicon Valley.

Most frequently used UNIQUE words

As we can see in the previous worldcloud, there are many words which are actually shared in both wordclouds. That makes all the sense as there are many areas which will be forever connected with universal basic income. But I went one step further and decided to create wordclouds which contain only unique words with don't appear in the opposite wordcloud.

^{Most often UNIQUE used words in tweets containing word "universal basic income" before and after 2015-12-31.}

My subjective comment on the chart: One very interesting thing to notice here is the word podcast in the second wordcloud. I totally believe it's in there because of Joe Rogan Experience #1245 with american president candidate Andrew Yang who's main campain is based on UBI implementation. Also "AI" got a huuuge boost. It's also nice to see Iceland in the before graph which had a strong debate about UBI in 2014.

* words excluded from all 4 wordclouds are: yii,bit.ly,.ly,universal basic income,UBI,ubi,universal,basic,income

About project

This series of posts shows the power of machine learning and it's application in the real life. It also makes kind of symbolical point of analyzing Twitter and publishing it here on Steemit. Technology of the future is being used on the social media of the future ;)

Get your report

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Twitter sentiment analysis reports are being sold for quite some dollars in the world outside of Steemit. In our tiny word of Steemit, such price would be way too much - that's why I'm offering to generate& send you a report with your chosen keyword and dates for a laughable price - 5 STEEM. Order 3 and get the fourth one for free :)

Interested in how's you favorite coin doing on Twitter? Or favorite athlete? Politician, actor or clothes company? . Just DM me and you'll get the full report under 48 hours :)

Hope you enjoyed! Matko.