Yesterday, I began sorting boxes my parents delivered over a year ago after Jacob and I moved into the house. One of these boxes was a mix of school papers, including some of my earliest poetry. These are the poems I’d forgotten about.
Based on the folder, they were written sometime between 2000 and 2005, so I was between ages 10 and 16. As I go through these some will be hilarious and worth keeping for that reason alone. Some are insights into the mind of an adolescent alive during that time period and the observations they chose to write down.
I’m started with a series of four untitled poems, four lines each. Now, enjoy some childish, painful forced rhyme.
Sometimes I’m jealous Of those who win All the things That might have been
We tend to love What time we waste The past is delicious; Time? An acquired taste.
Oh Glory Be! In times we say As all hope drifts far away It is the clouded sea she took And my heart – a little brook
Sometimes the world just passes by A shock so sudden we can’t cry With each candle on birthday cake Comes regret with decisions we make
Thank you for taking the time to read these today! Without you these posts don’t carry the same meaning. Have you ever found any of your old writing? What did you think of it? What do you think of these? I’d love to hear in the comments!
If you’d like to see more of my forgotten poetry, please like, comment, and/or share this post. It helps me know what content my readers are most interested in seeing, so I can better know what to share here.
Previously, when I discussed onemilliontweetmap.com I left off a feature from my discussion. Today, I’m going to show you why.
Time for The Sentiment Tool.
To get an idea of how people feel about a certain keyword or hashtag you can look at how it is being used in association to the connotations of the words in the same tweets. The idea is that words within a language have connotations that can be used to convey how someone is feeling on a subject. The above map is the baseline prior to implementing any keywords or hashtags to narrow it down.
For these I decided to include the Daytime/Nighttime layer since this is a 24 hour visualization. I’m not doing top 5 countries for these images, and instead am focusing on the overall sentiment patterns and I’m going to explain why I’m not the biggest fan of this tool.
1. Keyword: Party
This one is fun because it is so ambiguous. “Party” can be related to a birthday party or a political party. Some would claim the word “party” has a preexisting positive connotation. Some regions of the world are more negative than others. Africa and the Americas are the most negative based on the tweets from the past 24 hours.
What’s fun about this tool is that you can zoom in on specific tweets to see what some of the positive vs. negative examples are. What I did find was that there’s a lot of inconsistency as to what is counted as “positive.”
Here’s a false positive via Hawaii:
Let’s try another one! This tweet turned out to be a true positive and is way more wholesome:
What about the negatives?
2. keyword: Election
Well, more people feel negative or neutral, rather than they feel positive on a global scale. That’s… not good? Maybe the world hates politics! Election itself has a fairly neutral connotation, so I would have expected a neutral sentiment overall.
Based on example 1, we can tell that tweets inclusive of the string “Hitler” can still be detected as positive with a combination of words that still convey a positive connotation by the sentiment algorithm used by onemilliontweetmap.com – so, there’s some work that can be done in regards to reliability.
In summary, many of the positive, negative, and neutral tweets in the US have to do with the suggestion that Election day be moved. Due to consistency, I’ll spare the examples.
To get an idea of how untrustworthy the sentiment tool is I decided it was time to do a test.
Okay. Hold up.
This was meant to be my 100% of people don’t think this is happy. Something isn’t right here. Let’s look into this.
False Positive #1:
False Positive #2:
Most of these false positives are celebrations of life – cases where positive language is used in combination with language of grief. Clearly this confuses the heck out of the sentiment tool.
5. Keyword: Fantastic
So what about false negatives? Well, those can happen too! Using the keyword “fantastic” with the assumption of 100% positive results, I was able to find an example.
In this case it looks like the algorithm may have been confused by the word “missed”? Otherwise, I am uncertain as to why this tweet was counted as a negative sentiment.
The Sentiment Tool In Summary
It’s important to remember how flawed these tools are in the face of judging human emotion. While it’s powerful to be able to look at large populations to gain an understanding of their overall attitudes, as you begin to break it down everything falls apart. There’s too much nuance to trust an algorithm to determine what is objectively positive, negative, or neutral without additional data. Each connotations is determined by social research that is flawed and fails to capture the diversity present within language, instead focusing on a standardization model that homogenizes word sentiment. This is done by some set of people deciding the connotations for words within the sets their algorithms scan for.
Connotations around language are based in culture and regional dialects, rather than the denotations found in dictionaries. Here are some examples of positive things to say where I grew up that would not be interpreted that way elsewhere:
Well, she/he/they ain’t ugly.
This food is just terrible – I’ll do everyone a favor and finish it.
I’d hate to meet you under better circumstances.
What are some regionalisms from where you grew up that don’t match up with the assumed connotations of words? Do you think these would be confusing to someone not from there?
What is your opinion of the sentiment tool? Would you find it helpful in writing? Do you think it’s helpful or does it introduce more confusion?
Let me know what you think in the comments!
Thank you so much for reading this and I hope you have a fantastic rest of your day. If you like what you read, please consider liking, commenting, or sharing. This helps me know which posts my readers enjoy the most. And as always – thank you so much for taking some time out of your day to spend with me.
Content Warning: This poem addresses child trafficking and child laundering. This poem is based on true events. Reader discretion is advised.
His Name Wasn’t John
We play in his backyard Conquer the big rock Footholds covered in Light blue-grey lichen While he says Strange things To a five year old mind He talks of Africa He talks of memories: His parents still alive (Not the white couple inside) He says They brought groceries He says he didn’t always speak English Before the human trafficking, [I’m sorry] Adoption.
Thank you so much for reading this poem. If you found yourself moved, please consider liking, commenting, and/or sharing it with others. Truly, I am grateful for the time you spent reading my work. While you’re here, if this sparked anger in you as it did for me when I realized that this happened to a childhood friend of mine, please check out UNICEF’s page on child traffickingand consider getting involved with Save The Children.
Cultured like roadkill On a hot summer’s day Drive by high speed 18 wheeler fly by Accents rolling off tongues Cleaner than a sailors With the artificial faiths Of political bumper stickers The cults of dental insurance Filtering through Eisenhower’s veins With flashbulb cameras And Hollywood trends They choke on their implosion Exposed maggots chewing away The rotten insides Of the country we mowed down On our way to a National Park
Thank you so much for reading my new poem today! If you found connection to its words, please consider liking, commenting, and/or sharing it with others. Truly, I am grateful for the time you spent reading my work.