This is my 2nd Ngram viewer which looks at how commonly found the country names Venezuela, Uruguay,and Chile have been in the English language books that have been uploaded up to 2019. It clearly shows that Chile is the most popular country followed by Venezuela and then Uruguay. This is the same experiment as above, just done with other country names.
This is one of the more advanced settings available in Ngram viewer. For me, I used the verb of comer, which means to eat, in Spanish. The phrase "comer una galleta" is to eat a cookie. I did an inflection search to see how common the verb for eating a cookie is used. PS. It's most often used in the present sense.
For this Ngram Viewer, I did a wildcard search to see what the 10 most common subsections are for the word after Puerto. I am writing a paper on Puerto Rico and this is helpful for me to understand the different answers that may come up doing online searches for secondary and primary sources with Puerto Rico. It was also just interesting to see what is associated with the word Puerto beyond Puerto Rico.
I decided that the first display are the displays that I like. I really enjoyed the top part of the Voyant Tools Displays that allowed me to see the most common terms, words, and segments related to specific words/individuals/places based on their repetition and usage. This particular screenshot shows the display of the most central characters in the book The Red and the Black by Stendhal.
This is the document segments of Madame which refers to one of the main characters in the novel. In particular, it this document segment shows the change in Madame's relevance and presence towards the plot, which quite interesting in considering how direct presence does not always show the importance of the character because of the other aliases awarded to her.
This Term Cloud looks at the most common terms throughout the book, which is super interesting as someone who is interested in the humanities because it shows a lot of the most important themes because of their interconnectedness to the terms used. This would definitely have been super helpful to be able to use in English classes in the past to understand the most important themes visually.
This final screenshot is of a word cloud which shows the most important words in the books. The central word is Julien: who is the main character of the novel and shows the other words that are most commonly used which does very much what the previous term cloud display does but visually with the size of the space the word occupies. These are great visualization tools to look at how the written composition of a book can be so easily shown in different visual forms.
Screaming, especially in lots of recent vernacular cases, has become a very positive word that means happy screaming or screaming in cases of laughter.
Crying can also happen positively and does in many instances.
Avoid is also weighted positively but this is a very negative word. Not really sure why it has a 1 instead of a negative number.
Brisk is weighted positively but is a word that I have only ever heard used in the context of contorting the weather as being quite aggressive and not the best. Not really a positive word at all.
Please note that all of the quotes below are taken from the book Atonement by Ian McEwan.
The First Instance Where the Sentiments are In Accordance and Pretty Good At Understanding What's Happening
This first quote is a quote does have negative sentiments which are identified by both of the sentiment AI tools.
Global Sentiment
SentiMood
The Second Instance Where the Sentiments are In Accordance and Pretty Good At Understanding What's Happening
This quote is a very positive quote, which both do find, though the Sentimood sentiment AI did find it to be positive but also negative which shows that it is not as good as the Global Sentiment tool which understood more of the complexities of the context of the quote.
Global Sentiment
SentiMood
The First Instance Where the Sentiments are In Disagreement
This quote is positive, which Global Sentiment does show. SentiMood, however, sees it as a very non-sentimental quote which it is not.
Global Sentiment
SentiMood
The Second Instance Where the Sentiments are In Disagreement
This quote does also have a sentiment but I think it's melancholic analysis was not easily understood by either of of sentiment apps. Global Sentiment found it to be a neutral statement, which it clearly is not, and I found Sentimood's assessment of a -8 score to be far too negative because the statement is not a totally negative statement.
Global Sentiment
SentiMood
The First Instance Where the Sentiments are In Agreement but WRONG
This statement is not neutral, which is what both of the sentiment AI found it to be. It is far more of an emotional statement that is filled with a melancholic and loving tone that, as humans, we understand. Both are wrong.
Global Sentiment:
SentiMood:
The Second Instance Where the Sentiments are In Agreement but WRONG
Once again, this is seen as both positive yet, the statement is very very sad. Once again, showing how both are unable to understand the context nor how tone is created through the way the words are phrased together. It is wishful but in a way that is escapist, which makes the quote incredibly sad. Once again, both of these sentiment finding tools are INCORRECT.
Global Sentiment:
SentiMood:
The First Proverb in BING
Bing Translate Spanish to English:
Bing Translate English to Spanish:
The First Proverb in Google
Google Translate Spanish to English:
Google Translate English to Spanish:
The Second Proverb in Bing
Bing Translate Spanish to English:
Bing Translate English to Spanish:
The Second Proverb in Google
Google Translate Spanish to English:
Google Translate English to Spanish:
El Catedral del Mar in Google [A Bad Translation Back Into Spanish]
Google Translate Spanish to English:
Google Translate English to Spanish:
El Catedral del Mar in BING [A SLIGHTLY better translation back into Spanish]
Bing Translate Spanish to English:
Bing Translate English to Spanish:
PAULA by Isabel Allende in BING [Another Instance Where Bing is Slightly Better at Translating but Still Not Great]
Bing Translate Spanish to English:
Bing Translate English to Spanish:
PAULA by Isabel Allende in Google [Google Translate Does Not Reverse Translate Well At All: The Final Example]
Google Translate Spanish to English:
Google Translate English to Spanish:
I decided to train a model to learn my name, though I'm not sure how successful this was because it was quite difficult to understand what the output was. This could be because of the lack of good microphones and audio space that I have in my dorm room but, it was interesting noticing the difference in output components as I recorded more class 2 samples to add to the training model.
Find the link to look at my model here!
Playing With Dancer Pose
I decided to train a pose model to learn dancer's pose. The pose model was far easier to understand its successful output than the audio model, though it still took a little bit of learning to understand what it meant to learn the pose. Also, sorry for the inability to hold the post and take a screenshot.
Find the link to play around with my Dancer's Pose Model here!
