ekm4, Ellie Marquardt

Part 1: This ngram uses a function to compare the use of mayor in English and Spanish. The English mayor is used significantly less frequently than its false cognate, mayor, in Spanish.
I was interested in searching for the prevalence of youth sports terminology because I've been reading a lot of articles recently about the reckoning that youth sports is having in regard to the way we value performance over people. The youth sports industry is very young (and honestly exploitative), so I wanted to see if I could find anything interesting about the way it's referenced in the ngram data. This particular ngram pulls together the data for youth, sports, and youth sports. I also separate the terms below to see which ones have the most influence on the prevalence of the usage of all these terms.

Part 2: The book that I used is Sense and Sensibility. Here is a word cloud:

Here is the prevalence of names organized by section of the book:

Here is some basic information about the words in the book and the file:

I enjoyed being able to find information about the repetition of phrases within the book. I also really enjoyed being able to measure how common different terms were used in the book. "Miss" was an exceptionally common term - seems accurate coming from an Austen novel.

Part 3:
1. Affected has a -1 rating but honestly could go either way. You can be positively affected as well as negatively affected by something. Another word that could go either way is "skeptic." Sometimes it is good to walk into environments with somewhat of a skeptical (at least inquisitive) mindset. For example, it is very helpful to be skeptical of emails that are too good to be true (You won a free iPad!!).
2. A word that is weighed wrong is "sick." Colloquially people will refer to something as "sick" meaning cool or interesting vs just meaning ill. Another word is "solemn." It is rated at -1. Whenever I think of solemn I think of important occasions, but not necessarily bad ones.
3. Sentence: She was generous, amiable, interesting: she was everything but prudent. (Sense and Sensibility).
Sentimood: Score 4 Comparative 2 2 positive: generous,interesting... 0 negative: ..
Commercial analyzer: This text is Positive with a confidence of a 100 percent. The polarities detected in it are in agreement. The text is subjective and without irony.
Sentence: She could consult with her brother, could receive her sister-in-law on her arrival, and treat her with proper attention; and could strive to rouse her mother to similar exertion, and encourage her to similar forbearance.
Sentimood: Score 2 Comparative 2 1 positive: encourage... 0 negative: ...
Commercial analyzer: This text is Positive with a confidence of a 97 percent. The polarities detected in it are in agreement. The text is subjective and without irony.
4. Sentence: Mrs. Dashwood, who could not think a man five years younger than herself, so exceedingly ancient as he appeared to the youthful fancy of her daughter, ventured to clear Mrs. Jennings from the probability of wishing to throw ridicule on his age.
Sentimood: Score 4 Comparative 1.33333 3 positive: youthful,clear,wishing... 0 negative: ...
Commercial analyzer: This text is Negative with a confidence of a 76 percent. The polarities detected in it are in disagreement. The text is subjective and Ironic.
Sentence: Mama, you are not doing me justice.
Sentimood: Score 2 Comparative 2 1 positive: justice... 0 negative: ...
Commercial analyzer: This text is Negative with a confidence of a 92 percent. The polarities detected in it are in agreement. The text is objective and without irony.
5. Sentence: Marianne began now to perceive that the desperation which had seized her at sixteen and a half, of ever seeing a man who could satisfy her ideas of perfection, had been rash and unjustifiable
Sentimood: Score -2 Comparative -2 0 positive: ... 1 negative: rash...
Commercial Analyzer: This text is Neutral with a confidence of a 100 percent. The polarities detected in it are in agreement. The text is objective and without irony.
In my head, this text should have a positive denotation since there is a lot of character growth here.
Sentence: She only wished that it were less openly shown; and once or twice did venture to suggest the propriety of some self-command to Marianne.
Sentimood: Score 0 Comparative 0 0 positive: ... 0 negative: ...
Commercial analyzer: This text is Positive with a confidence of a 100 percent. The polarities detected in it are in agreement. The text is objective and without irony.
I find this passage to be wistful and sad, not neutral and definitely not positive.

Part 4:
1. Works (somewhat) well:
Google Translate: You know him then, said Mrs. Dashwood. --> Spanish: Entonces lo conoce, dijo la Sra. Dashwood. --> English: Then you know him, Mrs. Dashwood said.
Bing: You know him then, said Mrs. Dashwood. --> Spanish: Lo conoces entonces, dijo la sra. Dashwood. --> English: You know him then, Mrs. Dashwood said.
Google: And what sort of a young man is he? --> Spanish: Y que clase de joven es el? --> English: And what kind of young man is he?
Bing: And what sort of a young man is he? --> Spanish: Y que clase de joven es? --> English: And what kind of young man is he?
2. Works... not so great:
Google: A very decent shot, and there is not a bolder rider in England. --> Spanish: Muy decente tiro, y no hay un jinete de rocas en Inglaterra. --> English: Very decent shot, and there is no rock rider in England.
Bing: A very decent shot, and there is not a bolder rider in England. --> Un muy decente disparo, y no hay un jinete mas audaz en Inglaterra --> English: A very decent there is no bolder rider in England.
Google: She was prepared to be wet through, fatigued, and frightened; but the event was still more unfortunate, for they did not go at all. --> Spanish: Estaba preparada para estar mojada, fatigada, y asustado; pero el evento fue aun mas desafortunado, porque lo hicieron no ir en absolute. --> English: I was prepared to be wet, weary, and scared; but the event was even more unfortunate, because they did don't go at all.
Bing: She was prepared to be wet through, fatigued, and frightened; but the event was still more unfortunate, for they did not go at all. --> Spanish: Estaba preparada para estar mojada, fatigada, y asustado;pero el evento fue aun mas desafortunado,ya que lo hicieron no ir en absoluto. --> English: I was prepared to be wet, fatigued, and frightened; but the event was even more unfortunate, as they did not going at all.

The translation services were fine for translating simple sentences with common vocabularies and grammar structures. They struggled a lot more with complicated sentences with many clauses, especially when the tenses changed. Something extremely noticeable was the change in 1st and 3rd person -- since Spanish doesn't use pronouns in the same way English does, the programs struggled with movement between languages. The programs also struggled with figures of speech since the direct translation often doesn't make sense in a new language.
I didn't see an super meaningful differences between the services -- generally if one had an error, the other service made the same error. The biggest difference is the ordering of words or the use of articles. This reminds me of elementary and middle school -- I went to a Spanish immersion school, and we were never allowed to use Google translate, only a Spanish dictionary. The teachers wanted to teach us how to think in Spanish, not translate between English and Spanish, because nuance is lost between languages.

Part 5:
For this part I wanted to start off simple to get an idea of what this does. I started with a picture of my face with a mask, and then a picture without a mask. It was able to differentiate very quickly which was which. I only used about 60 photos! Here is a picture of how it worked:


I tried to see if I could teach the machine to distinguish between a hair part to the right and a hair part to the left, but unfortunately even after 400 samples, the machine still hadn't figured it out, so I had to throw in the towel. With considerably frizzier hair after (thanks google).
Unfortunately I procrastinated and my friends are asleep so this will not be the most interesting slide... I tried this with a sweater and without. The machine picked it up pretty quickly!! I only used 100 per class.

Part 6