#i hate oracle SQL

[manrippedapartbydogs]

high offf rthe clan man

[leahnardo-da-veggie]

Masterlist of Writing

Oh my goodness this took so long to finish haha! Anyways this is a compilation of everything I've written so far, it will be updated as I go along. Btw Here's an about me tag game I once did :)

Worldbuilding:

Geography

Linguistic Post

Linguistics Part 2!

Hygiene, Healthcare and Hieroglyphs

Ceredell

High Fantasy:

The Holy Crusader

Honey-cake

Deer-shade

A Thousand Lives

My Worst Nightmare

Tabitha-Who-Saw-the-gods

The Fae Prince

The Oracle and the King (aka the story of Iraela's sister)

Daughter

The Godhuntress & the Void:

The End of the World

The Beginning of the World

Old Friends

For Want of a Flower

Spirits:

The Spirit Emperor

No

Merida

History (Not a lorepost) (commentary included)

Put up a Sign

To My Friend: Or, a Letter from a Villain

Attempts at fluff/Writing experiments:

Pt 1 (fluff)

Pt 2(angst)

Ones Such As Us (romance)

Requests:

Public transport

Field Researcher

Urban Fantasy:

Tituba and the Darkness

It watched me without eyes

Now, now Dearie

The Devil Drives a Good Bargain

Goodbye

Russian Roulette Club

The Saga of Maizen, Shatterer of Worlds

Rage

The Serpent, part 1

Part 2

Part 3

The Wanderer:

Part 1

Part 2

Part 3

Impossibility

Home

Homesick

Fast Food:

Childhood

Adolescence

Travels

Realistic/Non Fiction:

On Reading

Bird In a Cage

Dawn

Crumpling Butterflies

Expressions

Love

Hate

Box

Can I?

An Ode to Tofu

Love, or the lack thereof

Cream Puffs

An Angel

Trophy Case

Popping

Rooster

Poetry:

Blame

The Blazing Sun

Severance

A Tribute to Ivander Montane (Based off @/illarian-rambling's character)

SQL (a joke, mostly)

Misc:

Mahogany (IDK what genre)

God (Sci-fi)

Will you listen, please? (Sci-Fi)

Spirit of the Hole in the Wall (Horror)

An Explorer's Log (Sci-fi)

Heroes (Superhero)

False-Moon (Fantasy but unrelated to everything else)

Lantern (fantasy unrelated to anything else)

Envy (genreless)

Have a nice day! (Joke)

Grass (NSFW, gore)

Take and Give (horror)

How to Become a Hero (don't ask)

Little Men in Taps (joke)

The Smile of Misfortune (realistic fiction)

Christmas Special!! (Don't read this until you've checked out everything else lol)

Halloween Special (Horror)

A Dream (excerpt from my dream journal)

FAQ:

Q: I'm new to your work... Where should I start?

A: I would recommend The Spirit Emperor as an intro to the universe, and Old Friends as an intro to my writing style!

Q: What is your favourite work?

A: It's not similar to anything else I've written, and the last bits aren't that great, but I have a personal soft spot for Heroes

Q: Have you looked into the Void that lies beyond all things?

A: No. And I'd recommend you don't either.

[surfacage]

what languages do you speak?

C# .NET, SQL, HTML, Javascript, UML etc

j/k

english, filipino; my friends have a running joke that i can speak japanese when i’m drunk as shit

[someoneintheshadow456]

Java only starts getting nasty when you throw SQL and Oracle into the mix. I hate my college for this.

I’m dying.

[twitteranalytics]

The method of approach part 2/3.

Data storage/ analysing it

This post is quite lengthy so I have added in a keep reading line. Don’t be discouraged by this it is still worth a read.

Note: at the minute the database side of this project is still likely to change as I am currently looking into Oracle storage as opposed to local storage. 

The database will consist of a minimum of 3 normalised tables (although this is likely to be more in order to store additional information relating to the data); there will be a table which will contain all the search text needed to filter down which tweets to retrieve (these may be hashtags, keywords or phrases). This data could be anything from a topical current affair in the news or a trend that is currently occurring on Twitter/ the internet. Another table will contain all the sentiment phrases that will be used to determine the nature of each tweet, for example this table may contain words such as hate, love, like, dislike, terrible, horrible, brilliant, great etc. along with whether these words are negative, positive or neutral. The final core table will be used to store the tweets that have been retrieved by the API call and this table will be updated each time the API call is used.

 The database will be stored locally on the host computer (the computer that is carrying out the API calls and analytics) and in the event that the database gets too big some data will be compressed and archived. If it is found that the database is still unmanageable the database/ data will be stored on an external hard drive with manageable chunks being accessed/ downloaded to the host computer at any one time.

 The database, API and Java code will be used together as follows; the table of search text will be looped through with each record in the table altering the API call code to change which tweets will be returned. If this proves to be too intensive or time consuming the work load will be split across multiple threads and the GPU of the computer being used to increase performance with each thread taking one record from the table. The results returned by the API call will be processed by Java code and then written to the respective database table using SQL which will be embedded within a Java class allowing for ease of access and method calling.

 The sentiment table will then be looped through, in the same fashion as the retrieval of tweets above, and compared to the results table where the data retrieved by the API will be stored. Each tweet/ record will then be assigned an overwhelming positive, negative or neutral feeling. These will be determined by the word(s) which the data contains in correspondence with the sentiment dictionary table.

 This new dataset will then be used to carry out analytics. The analytics may include, but is not limited to, determining the sentiment of each text phrases and how this changed over time, showing the number of tweets over time containing certain text phrases, showing the most used/ unused emotional word in tweets from those present in the table and how these have changed over time, the number of emotional words used in tweets and the trend of this number relating to the overall sentiment of the tweet. All of the analysis, where appropriate, will be represented in both figures and a graphical representation. In order to do this tools such as TweetStats and Rapid Miner may be used. TweetStats can produce histograms displaying information such as number of retweets (and most common retweeting users), number of replies (and most common replying users), tweet density and interfaces used (Instagram, Twitter for iPhone etc.). The data science tool Rapid Miner can be used for providing graphical outputs of the analysed data.