https://www.youtube.com/watch?v=xJTz1UbpSXM
mq3001, Madison Qualls
I struggled to get my images to show up on the webpages, and I also struggled to get the mood detectors to work.
Part 1
This graph shows the rise in the Search of Donald J. As in Donald J. Trump in 2015 and on as when he was running for and became president. I used the advanced feature, "Wildcard search" To find the most popular words following the name Donald to prove my theory that there would be an increase in the searching on that name, leading up to and during the 2016 election.
This other photo shows the popularity of the different endings of the word "lit". I thought that the word "lit" itself would have a rise in popularity around 2016, because the slang term became popular then but the data shown in the graph is not clear. I used the advanced feature "inflection" to show the word lit and the different endings
Part 2
I picked the book Jane Eyre, and this is the world cloud of the most used words of the book:
I thought it was really insightful to see the relative frequency of the words "mr." "mrs." and "miss" in the book. It was interesting to see how the words "mrs." and "miss" are almost never used in the end of the book. Which I think one could use to comment on how this may be another way for the book to convey its painting of a patriarchal society.
![](mr.jpg)
I thought the termberry was the most interesting because I liked how it showed the most common words, but when you hovered you mouse over a certain word, it showed the words that were most often used around it, on in the same sentence as that word.
Part 3
I think the words "Giddy" and "Futile" are weighted seriously wrong, and I think the words "pensive" and "shock" could go either way. They both are that the sentence "I was very happy to see my cute dog today" is very positive and the sentence "I'm upset because the weather was awful today" is very negative. The phrase "break a leg!" Was done wrong by Sentimood but done right by Meaning Cloud . The phrase "laughing my ass off" is deemed very negative for Meaning Cloud and equally positive and negative for Sentimood. They were both wrong and thought "That dance move was sick!" and "You're killing me!" were negative phrases.
Part 4
"Love you more than life itself" worked well in both Google and Bing with the translation being "Te amo mas que a la vida misma". Also the phrase "All for one, and one for all" translated well in both and the translation was "Todos para uno y uno para todos"
The phrase "Just in the nick of time failed for both" with them translating back to English as "just in time". Another phrase that failed was "fit as a fiddle" which after being translated to Spanish and then back to English came back as "In good physical condition".
Translation services overall for Spanish work very well, and would be very useful in practice. I also don't see any meaningful differences in the services.
Part 5
For my experiment I decided to try to teach the computer the difference between a peace sign and a "rock and roll" gesture. I did this because I just thought it would be fun, and to see how distinctly different the hand placement had to be for the computer to tell the difference. I used around 90 samples of different variations of peace signs and rock and roll signs. The more samples I took the more in improved. Here are the screenshots of it successfully identifying the difference between the two signs:
![](rock.jpg)