• Reseach wikidata and sparql
• Create a wiki page

On frontend put: From time, end time, search button

Backend: Display a page with 100 tweets from the given time interval

Learn about the basics and structure of the database.

Try CRUD on a python script

Deploy the application to prod.

Set the DB as postgres that was set on Heroku, for posts

Test every functionality

Work on:

• the general structure of Django
• MVC
• dependencies
• a basic tutorial / implementation

share findings on Wiki page.

The page should contain,

• a welcome message
• buttons to direct to login or signup pages

Design the front page and create a mock up
Include:

• fields for user input
• tables for data output
• charts and graphics for visualization

Direct the DB of application from sqlLite to Postgre.

Implement this depedency on the docker files.

Schedule a job for data stream. Same job that is currently running on local can be used.

300 posts every 30 mins.

• Create the network: which is better, weighted or unweighted?
• Consider the ways to display it on front-end
• Decide which indices to display on frontend

Using the word search results page as a template, update views.py and templates to include display of data results.

Implement entity linking via TagMe in the code
Try on a random tweet

Find a bootstrap design theme online, free.

Implement it on the application for the general style. Use "static" folder to manage and reference the implementation.

Decide on the volume and time interval for the data collection.

Implement a scheduled job for data transfer to DB

• Find a decent package for sentiment analysis
• Decide on the tresholds for Neutral
• Implement on code

Required fields for registration

• First name
• Last name
• User name
• Password
• Password confirmation
• E-mail

Once registered the sign up page should log the user in and direct to main page.

Finalize the software requirements specifications. Include:

• Functional requirements
• Non-functional requirements
• Mockups
• Scenarios

Put in on Wiki.

Check Piazza for implementation details

Evaluate the data cleaning methods. Decide which ones to include. Decide on the order of implementation

• Install Project Libre
• List all tasks in mind
• Link the tasks together
• Assign tasks to weekly milestones
• show on Gant chart

Transfer variables in the .env file to config vars on Heroku.

Incorporate in code the new source of variables

test user login and log out of the site, fix if there are any irregularities

Create the .env file and add every sensitive info in that. Commit changes

create a student account on Azure.

deploy the dockerized app as a web app to cloud

Create a documentation on the Wiki page to include;

• function and operation of each feature on the pages
• warning about usage if there are any
• suggestions for future development

The table should contain the following fields:

• Original and cleaned post content.
• Favorite / retweet counts
• Sentiment Analysis result
• post date
• emoji content
• mentions in the post
• entities in the post
• owner of the post
• whether post is a retweet or not

See if the page is really necessary. If not, consider replacing it with simple log out.

As a single batch job, transfer all data collected on local POSTGRES db to the DB created on Heroku.

While transferring, re-check all retweet and favourite counts, send updated data

Before submission, finalize all tests, regarding every point of the application. Fix if anything left to fix...

Due to AWS policies, connection to snscrape servers are banned.
The date search of the application does not work because of it. Find an alternative way for date search.

• Decide on the titles on the bar
• Implement in the application

Modify readme file with project details

Research git and document a summary on wiki

Write the code to implement the data cleaning strategy.

• Have the methods modularized in a separate class that can be imported.
• Keep the custom stopwords and abbreviations on different files and read them into the code.

• Clone repo
• Connect the account to VS Code.
• Install Markdown extension to VS Code

• Setup a trial DB (mongo cloud is ok)
• load some mock data
• try CRUD by script
• if you find stg noteworthy/ out of ordinary, create and share on wiki

Create a postgres db on Heroku. The model for that will be the local DB that is currently being used for storing posts.

Look for ways to transfer data form local to Heroku. If possible sent previously collected data.

Before the meeting with customer questions for requirement elicitation will be prepared. Questions may be collected on a wiki page that is open to contribution from every member.

Questions should be prepared to enlighten the following topics (at minimum):

• The product they have in mind
• The medium it will run on
• Performance requirements
• Target group of the product
• User interface design
• Deadline of the project (milestones and deliveries up to that)
• Amount of deliveries and the minimum content
• Budget of the project
• Freshness of the data to be used
• Regulatory constraints, if any

Test with the Twitter API for:

• trending topics
• limitations for the account permissions
• stream data
• historical data
• searching

Research on possible Sentiment Analysis methods and packages.

Explain reasoning for the package chosen.

Dockerize a trial django application.

Main purpose is to understand the docker-compose.yaml, requirements.txt and containerization of the application.

Run the application using Docker.

• Impelement the process to clean& analyze and then store data at the Postgresql database.
• Use the classes created for cleaning and analyzing

Using the word search results page as a template, update views.py and templates to include display of data results.

Should have fields for username and password.
Must direct to main page if user already a member or else to sign up.
Display if there are any error messages.