Clicata, CJ Licata

The Sentimood part was a bit complex. I thought there were a lot of steps and felt a bit overwhelmed. But, I did my best to complete all the tasks. I thought this was a lot of fun, but took a lot of time.

Part 1

In the first graph, I chose to the key words "college of" to show how often certain colleges are mentioned. It shows the data from 1800 to the present day and records the frequency on which colleges were mentioned across libraries and literary works. The advanced feature I used was the wildcard feature. The importance of this variation is I can put in "Colleges of *" in the search bar and it will produce the top 10 combinations of colleges. T he most popular colleges discussed from number 1-->10, but it was unique because it discussed colleges in terms of area of study. In the early 19th century, College of Physicians was clearly more popular and talked about. The next growth was in the College of Agriculture, that was peaking in 1910 and slowly decline until 1950.

The second graph shows the use of the work "drink" when in conjunction with water. I was curious to see what tense is most and least frequent. I used the inflection search as my advanced feature to see the grammatical differences with the action "drink water." The results were interesting, but not quite shocking. "Drinking water" was by far the most popular that started to gain traction around 1870. It peaked from 1990 to 2000 and was towering over other variations. The next popular were "drink water," "drank water," but I was most shocked that "drunk water" was before "drinks water."

Part 2

The book I chose was Romeo and Juliet by William Shakespeare. It has close to 30,000 words and is clearly one of the most famous literary works of all time. My first image displays a word cloud. It details the most frequent words of the text. What I thought was unique was how the size of the font reflected the large or little amount the word was used. Clearly, Romeo was the word used the most. There were a lot of tools that I found particularly cool and useful. The correlation and scatterplot features were awesome because they displayed words or sentences that came up a bit. The different visual techniques really enhanced the understanding because it helped you comprehend it in different ways. The two features I liked the most and decided to include were the "trends" and "loom" features. The "trends" chart displayed five words and their frequency throughout the text. It was a mini scatterplot with colors that made it really easy to see. Some of the other charts that I tried out were hard to see visually because of so much data. The "loom" chart was my personal favorite, however it is definitely an overflow of data. It illustrated words in alphabetical order and their frequency. My chart showed words that started with the letter "M." If I scrolled up, it would show "L" or other letters trending to "A" or vice versa the opposite way. It was a unique feature to see which words Shakespeare tended to use more often than others.

Part 3

The word sick is only used negatively by Sentimood. Whether I describe sick as something cool or an ill person it is only negative. Also, some instances like the word stop has an issue because the sentence is not read. Using certain words in a totally positive sense still gets counted as a negative. A flaw of Sentimood. Below is the first example of sentimood and commercial system to agree and be correct on a sentence.

Another example.

This example is a flaw because it is clearly a negative paragraph. Instead, it mentioned there was only positivity, etc. They agree but are clearly wrong. Another example below.

Part 4

Using google translate and Bing's translation service can show differences in their systems. The first novel quote I tried was A Tale of Two Cities's "It was the best of times, it was the worst of times..." The results were identical. They both produced "Fue el mejor de los tiempos, fue el peor de los tiempos." Another example that worked well was the input "O Romeo, Romeo, wherefore art thou Romeo" Romeo and Juliet." Both google translate and Bing produced identical results again! However, two results did not produce the same results. Wayne Gretzky's "You miss 100% of the shots you don't take" produced different words used. Google translate used the word "disparos" for shots. Disparar is a verb that refers to shooting, but with guns. On the other hand, Bing used the word "Tomas" for shots and the verb "tomar" which means to take. Google translate used "hacer," which essentially means the "shots you do not do." The last example that came out awkwardly attached below is "Float like a butterfly, sting like a bee. The hands can't hit what the eyes can't see" by Muhammad Ali. Bing's verb modified the implied noun weird. It read "It floats...it stings" etc. Which made the subject be "it." Google translate had no error. Overall, my translation service does a pretty good job. It would be useful in practice to do this more often to improve my skills and double check to make sure google translate and bing have a pretty seamless and consistent translation. I don't see meaningful differences, but google translate was a bit more automatic with switching the languages...and I enjoyed that.

Part 5

For part 5, I decided to use a hat as a prop and position it differently and turn it the opposite way. I was curious how the camera would pickup the prop and recognize it. I chose the hat because of its symmetry and color. It worked well, and would be curious to try other examples with more complexity to see what does not work well.

Part 6

Thanks for tuning in!