Vr1834, Vanessa Rivkin

I wasn't completely sure about some of the formatting here, but otherwise I thought the lab was clear and interesting.

Part 1

In this first graph, I measured how the name Vanessa has been used in English during the time period 1800-2019. I used the Wildcard search tool, mostly intending to search for famous Vanessas, but ended up with a good amount of the name used directly in-text. I did end up with the results of some famous Vanessas mentioned, including Vanessa Bell and Vanessa Redgrave. Secondly, I combined ngrams with the + symbol. I compared the difference in occurrence between different variations of "mother" and then had calculated the distribution of the idea as a whole. The word mother is much more commonly used to express the idea than any other name we use in daily life for the maternal figure.

Part 2

For the second part, I chose to use the book "The Phantom of the Opera" by Gaston Leroux. I liked the symbolism of the names in the word cloud that I chose. I thought a lot of the tools were very interesting, but I especially liked looking at the correlation between the characters Christine and Raoul's names and some of the most common words their names are associated with. The tool that showed the most common phrases associated with a name was intriguing to see, especially when those same phrases repeat themselves within the book. Lastly, I thought the frequencies of names was cool to see, reminding me of what we did with last week's lab.



Part 3

I found that the words joke and crazy could be either negative or positive. I think the weighing for words like "admit" or "silly" aren't the best, as there are a lot of different connotations that easily make the words positive.
Same:
Tweet-(both negative)
"if you’re a regular at a restaurant just know the staff has an insane secret nickname for you"
Differ:
Random Sentence-(both positive)
A quiet house is nice until you are ordered to stay in it for months.
Jane Eyre-


Phantom of the Opera:


Agree but Wrong:
A Modest Proposal-


Ramdom Sentence-( programs said it was negative/without irony)
For some unfathomable reason, the response team didn't consider a lack of milk for my cereal as a proper emergency.

Part 4

Examples that work well:
1. I read in a book once that a rose by any other name would smell as sweet, but I’ve never been able to believe it. -Anne of Green Gables
2. “Whenever you feel like criticizing anyone,” he told me, “just remember that all the people in this world haven’t had the advantages that you’ve had.” -The Great Gatsby
Examples that don't work well:
1. I detest rude, unladylike girls!” “I hate affected, niminy-piminy chits!” turns into... I hate rude and unladylike girls! " "I hate affected girls, niminy-piminy!" -Little Women
2. “Well, this is a pretty piece of business!” ejaculated Marilla. turns into... "Well this is a good deal!" Marilla exclaimed. -Anne of Green Gables
I translated the quotes into Spanish and then back into English again. The translations actually worked a lot better than I thought they would. In general, the translators are useful in practice, mostly barring when it comes to specific tenses or taking words literally. I did see a difference between services, and where usually I always use Google Translate, I noticed that Bing's translations stayed so much more accurate.

Part 5

I had fun with the two experiments I conducted, though I would probably have to add a good amount more of content for them to be more accurate. First, I did an image test, testing how well the program could distinguish between the Yale and Princeton logos as well as their other merchandising. I used images of items I had in my room(I'm from New Haven, so I actually did have a few Yale items handy). I chose this experiment to see whether the program was more based on factors like color vs actually imagery. The program seemed to be working fairly well by the end, as it correctly identified some of the items and posters and such logos in the background of my room. With more pictures, the machine responded more correctly. For my second project, I used the voice feature to see if the machine could distinguish between my voice and the voice of my roommate. We both sang into the microphone and with a certain number of voice clips, the machine could fairly well tell the difference between who was talking. If anything, this worked better than the first experiment.