In the image above, I used a wildcard search to display the top ten substitutions following the phrase "President of the" between 1800-2019. For this image, I made my search case insensitive. Obviously, "President of the United" was the most frequently occurring substitution, but interestingly, there were many other applications of the phrase "President of the."
In the image above, I used a dependency search combined with a wildcard to understand which nouns are most likely to follow the word "cross" between 1800-2019. I made my search case insensitive. I am most fascinated by the presence of the phrase "cross Atlantic," which appears to be consistently popular across history.
For this section, I selected Mary Shelley's Frankenstein as my text. I found the Cirrus to be an interesting visualization of various recurring themes from the book, including the idea of man vs. monster and the recurrence of the word "life" in many contexts. I also enjoyed playing with the Trends feature to note the concurrence of particular words. For instance, the words "father" and "life" oddly correlate quite a bit across the book. Finally, I also enjoyed using the TermsBerry tool to view the number of occurrences of certain words and how often the word is used in the same phrase as another word.
Above is an image of my Cirrus cloud.
Above is an image of the correlation of the words "father" and "life" across the span of the book.
Two words that could either be positive or negative:
Example: The ridiculous nature of his costume had us in stitches vs. His ridiculous requests were unfulfillable.
In these examples, the word "ridiculous," which is negatively interpreted by Sentimood, has positive and negative connotations in these sentences.
Example: The intense tennis match gave us both a great workout vs. The intense nature of the interview gave me anguish.
In these examples, the word "intense" has a positive connotation in the first sentence and a negative connotation in the second sentence, yet is interpreted as positive by Sentimood in both examples.
Weighting seriously wrong:
Sentences: He had no idea of the exciting surprise that he was about to receive. AND The scary nature of horror movies is actually what excites many people to watch them.
In the first sentence, the Sentimood analyzer automatically assumed "no" was a negative word, despite the positive connotation it has in the overall context of the sentence. In the second sentence, the Sentimood analyzer again incorrectly tags the word "scary" as a negative word, even though the sentence later states that that's what makes horror movies exciting.
Agree:
I used the following text from my college essay:
I loved finding auction houses. Each search seemed like a historical treasure hunt. I felt that every time I found an auction house, I helped exhume the slaves who had silently passed through without any recognition. This ad for G.J. Foreacre & Co. was the beginning of another mystery. I had the what. Now I wanted to identify the where.
Sentimood gave my paragraph a positive score of 7 and noted at least three positive words. The meaningcloud tool also gave my paragraph a positive score of 100 percent.
Disagree:
Sentence: The intense nature of the interview gave me great anguish.
This sentence is given a positive score of 1 by Sentimood, with 2 positive words countering 1 negative word yet is given a very negative score with 98 percent certainty by meaningcloud.
Both agree but are wrong: This is a sentence from Mary Shelley's Frankenstein: "I had admired the perfect forms of my cottagers: their grace, beauty, and delicate complexions; but how was I terrified when I viewed myself in a transparent pool!"
Sentimood gives this sentence a positive score of 4 with 3 positive words as compared to one negative word, and meaningcloud gives this sentence a positive score with 94 percent certainty. Yet, when one reads the sentence, it is obvious that the sentence has a negative meaning: though the monster can view the beauty of human beings, he realizes that he is wretched in comparison.
Example 1:
Article at this link: https://www.wsj.com/articles/rivian-prices-ipo-at-78-above-raised-target-range-11636502188?mod=hp_lead_pos1
English: Rivian Automotive Inc. priced its highly anticipated initial public offering at $78 a share, well above its raised expectations, valuing the electric-vehicle maker at more than $77 billion on a fully diluted basis, according to a person familiar with the matter.
Spanish Back to English: Rivian Automotive Inc.Its highly anticipated initial public offering is priced at $ 78 a share, well above its lofty expectations, valuing the electric vehicle maker at more than $ 77 billion on a fully diluted basis, according to a person familiar with it. with the matter.
This translation, aside from the few punctuation errors, appears to accurately convey the paragraph's ideas in the translation back to English.
Example 2:
English: Out of the frying pan and into the fire
Turkish Back to English: From the frying pan and to the fire
The translation back to English completely alters the meaning of the phrase and imparts a more literal interpretation on the idiom.
Using Bing Microsoft Translator:
Example 3:
Article at this link: https://www.bbc.com/news/science-environment-59220687
English: Despite pledges made at the climate summit COP26, the world is still nowhere near its goals on limiting global temperature rise, a new analysis shows.
Spanish Back to English: Despite promises made at the COP26 climate summit, the world is still nowhere near its goals of limiting global temperature rise, a new analysis shows.
This translation exactly imparts the same information as in the original English phrase.
Example 4:
English: It takes two to tango
German Back to English: There are two of them
In this example, the translated phrase completely loses its meaning and does not even impart a similar message to the original English phrase.
One of the biggest benefits of Google Translate that I noticed was the fact that it actually has an idioms functionÐit seems that editors have manually overridden some of the spanish-to-english translations to more accurately reflect the English idiom.
For this experiment, I tested whether the machine could distinguish whether I was wearing a hat or not. Initially, I took 25 pictures with the hat on and 25 pictures with the hat off. Unfortunately, the machine did not yet recognize whether I was wearing a hat or not. Therefore, in the next iteration of my experiment, I took roughly 50 pictures with a hat on and 50 pictures without a hat. Then, the machine successfully noted when and when not I was wearing a hat on the webcam with 100% certainty.
In this experiment, I tested whether the machine could differentiate between images or me eating and me drinking. From what I learned in the experiment above, I took roughly 50 pictures of me eating and roughly 50 pictures of me drinking. The machine was able to differentiate between the two scenarios with almost 100% certainty, which I found to be quite fascinating.