Thanks for a fascinating post! I am a rank Wolfram Alpha newbie and would never thought to apply Wolfram in this way. Plenty of things in your post to learn and to try.

Dear Alan,

well, you can create your own applet. The first thing is that you will need to decide where you want to save your data. For the OP I have used Google Drive, but I usually use Wolfram's data drop. Let's assume that you have linked your twitter account and Datadrop to IFTTT. You will also need to create a Databin and write down its id. For this illustration I will use my newly created private databank (FfI75hEn).

When you login to IFTTT you will have to make a new applet.

As usual you click on the this link. Then you choose Twitter and look at the triggers.

Then you have to choose "New tweet by specific user". After typing in the user name you confirm and will get to the "then" stage. There you choose datadrop and that you want to add something to a databin:

Then you fill in the details, i.e. the id of the databin and what you want to record:

You click create action and then on the next window

you click Finish.

With a bit of luck that should do the trick. If you have this in Datadrop it will be easier to import and analyse the data.

I hope this helps,

Marco

Kotaro-san,

Thank you for the suggestion, but I get the same result. Actually they are equivalent.

Flatten@Table[dataraw[[i, 1, All, 2]], {i, 1, Length@dataraw}] == 
 Flatten@dataraw[[All, All, All, 2]]
(* True *)

I tried restarting the kernel, but I still get the same error.

NetGraph::netinvseq: Invalid sequence {Which[<<1>>],<<43>>} provided to net.

I am running "12.0.0 for Mac OS X x86 (64-bit) (April 7, 2019)"

Any other suggestions?

Thanks, Rohit

Dear Rohit and Kotaro-san,

it also does run on my computer without any problem. I am not sure what goes wrong, but I seem to remember that I had something similar several times. What did appear to help is resetting Mathematica as described here.

Note that this comes with potential problems (e.g. if you have modified the init file).

Best wishes,

Marco

Dear Rohit,

Thank you very much for your message and the time it took you to run everything. It is possible that GeoIdentify worked better for you. But I have another hypothesis. As far as I can see you are based in the US. I ran the code from Scotland. The interpreter is a complicated thing. I think that if I type in "Aberdeen" it will take the one in Scotland (where I am based), if you use the Interpreter in the US that might give different results especially if you are in one of the "other" Aberdeens. This is based on the GeoIP; and I am right now not even sure whether I had a VPN server running at that time.

I think that we might get more homogeneous results if we set the $GeoLocation to the same place. We will still not necessarily get exactly the same because we use different Wolfram Server and there might be differences with requests timing out.

If anyone has a private cloud that would be interesting. Also, I could try to identify the cases that were not recognised properly and run the recognition again, or help manually.

All of this, of course, raises the question of error bars. It would be great to get a feeling for errors by reviewing say 100 detections by hand, ideally by 3 different people and figure out how often the algorithm is right (and also how often the people agree or disagree.

It is interesting how easy it is get get some results, but how difficult it is to get the exact same results.

At first, I was more concerned about the twitter handles. But that I could resolve. I think I posted a wrong table. When I rand the code the first time, I did not use the file "Trump Tweets.xlsx" only the ones with the numbers in the brackets, I believe.

I now ran it again and get your results:

Sorry for that. You can reproduce my error if you load only the first for files at the beginning, i.e.:

tweettextswrong = Join[dataraw[[1, 1]], dataraw[[2, 1]], dataraw[[3, 1]], dataraw[[4, 1]]][[All, 2]]

textcasesTwitterHandlewrong = TextCases[StringJoin[tweettextswrong], "TwitterHandle"];

twitterhandles50wrong = Normal[(ReverseSort@Counts[ToLowerCase /@ textcasesTwitterHandlewrong])[[1 ;; 50]]]

That should give you the numbers from my original post.

Again, thanks for pointing this out.

Best wishes,

Marco

Rohit-san, I hope this might help you.

tweettexts = 
 Flatten@Table[dataraw[[i, 1, All, 2]], {i, 1, Length@dataraw}]

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Dear Vitaliy,

I am really excited to be at the WTC. It is just fantastic to speak with so many innovative people making every possible area computational.

I can try the alternative way to do sentiment analysis. I've run it on some other dataset. There are many more analyses that we could do with the Wolfram Language here. In the new degree on Data Science that we will offer at the University we will have quite some example on Natural Language Processing. I hope that this will grow into a full course in the near future.

Me too, I am very much looking forward to speaking with you at the WTC. I hope we will have the opportunity to speak before that.

Best wishes,

Marco

Kotaro-san

thank you very much for your kind words. I am very much looking forward to the WTC. It is a fantastic event and I always learn a lot there. I

It is a pity that you cannot be there this year; I very much enjoy your posts and would very much enjoy the opportunity to talk with you.

Best wishes,

Marco

Dear Nam Tran-Hoang,

Thank you very much for your kind reply. You actually raise an interesting point when you say that I showed that their claim is incorrect. I actually do not even now how I would formally do that.

The point is that I don't know what exactly they did. I might have overlooked a link to their algorithm, but I could not find it. So what did they do? Did they include retweets? Did they only count direct mentions or also indirect ones (i.e. airport Heathrow -> mention of UK)? Did they do this by hand? Did they run a machine learning algorithm? I simply do not know. That makes it difficult to verify what they say for me.

I think that the Wolfram Language is a great tool to make these questions "computational", i.e. reproducible and potentially opens them up to criticism. In that I do there are lots of open questions; someone pointed out to me that Georgia might be a state or a country. If we use indirect mentions we need to decide whether Aberdeen is the one in the UK or one of the US ones, etc.

I am concerned about the state of our discussion culture. As many statements can just be put out there - often they are too hazy to address. I think that a computational language can contribute to a better discussion culture.

My analysis is far from perfect and has lots of bits that can and should be improved. But at least it is open to criticism - and I welcome that.

Thanks a lot for your time to read my post and your reply,

Marco