Browser extension that simplifies the Twitter interface and adds useful features

[DEPRECATED] Chrome extension that enforces the mobile web version of Twitter and improves its interface

✨ Adds UI improvements and new useful features to Twitter Lite

In this session, we'll show you how to use Python to automagically turn a PDF into an audiobook, without anyone needing to read the contents out loud to procure the audio. To achieve this, we'll use a few separate Python libraries—namely Pyttsx3 (for speech to text) and PyPDF2 (to parse PDF files)—and show you how to put it all together to obtain downloadable audio from your PDF input in a single command. We'll also demonstrate how you can customize this process to modulate output voice and speed. This technique can easily be then further refined for nuances of text and speech using other libraries and programming (including NLP/machine learning-based ones) Prerequisites: —Python (https://www.python.org/downloads/) —Visual Studio Code (https://code.visualstudio.com/download) ----------------------------------------- To learn more about The Assembly’s workshops, visit our website, social media or email us at workshops@theassembly.ae Our website: http://theassembly.ae Instagram: http://instagram.com/makesmartthings Facebook: http://fb.com/makesmartthings Twitter: http://twitter.com/makesmartthings #Python #Tutorial

A front end twitter scraper which gathers tweets at a rate of around a 1000 an hour (depends on specs).

Twitter bot using Java to make a refined search and then retweet results

Sentiment analysis using twitter API. Refined from course project from UW coursera courses.

This project is focused on analyzing Twitter data using the Twitter API and Apache Airflow. It enables you to collect and process tweets, and store the refined data for further analysis.

The Twitter Sentiment & Emotion Analyzer a Polished and Refined webapp, built with Python, JavaScript, and Flask, providing "Real-time" analysis of Twitter sentiment and emotions. With features to input custom tweets and analyze live Twitter streams and offers a comprehensive understanding of online conversations

A machine learning project that processes and visualized data from a CSV file and predicts whether a Twitter account is a bot based on a decision tree classifier that was refined using feature engineering

Twitter/X clone with a modern, professional design that captures the essence of the platform while adding refined aesthetics. The application features a clean black theme with blue accents, smooth animations, and intuitive interactions.

In this dashboard, I sourced Twitter data from DataWorld, then refined and modeled it using Power Query. I utilized various Power BI charts to present the data insights in an engaging and user-friendly format.

This project compares LSTM and BERT models for emotion recognition using Reddit's GoEmotions and Twitter datasets. LSTM uses Word2Vec embeddings and a refined architecture, while BERT is fine-tuned for sequence classification. Results show BERT generally outperforms LSTM, emphasizing model choice and data balance.

Assignment 2 - Passed with Distinction: Collaborating with the NHS, I used Python to analyze data on service utilization, staff capacity, and public opinion via Twitter. I refined business questions, conducted exploratory data analysis, and presented insights through visualizations and a detailed report to support NHS decision-making.

Mr. Number Ten Website - Engineered the website using Bootstrap with the help of HTML, CSS, and JavaScript. • Implemented Favicons as well as Twitter Cards for the website. • Applied website monitoring through FreshPing and Google Analytics. • Refined the website and its loading time through HTML W3 Validation and Website Speed Test.

SocialzR was built to allow media publishers to search, filter and curate Twitter content that can then be displayed on broadcast graphics and the web. SocialzR enables publishers to create complex keyword and hashtag queries to easily uncover high quality Tweets. Queries can be further refined by location, languages and more to create of the most relevant Tweets pertaining to that topic.

Series of scripts that monitors cryptocurrency mentions on Reddit and Twitter based on continually refined heuristics, as well as the average exchange price of hand-curated cryptocurrencies from coinmarketcap.com. The front end will continually monitor the script data and after a length of time we will run a general time series analysis and use the data to build an autonomous cryptocurrency trading model.

The purpose of this project is to compute the sentiment of text information - in my case, tweets posted in 2015 regarding US airlines - and answer the research question: “What can public opinion on Twitter tell us about the US airlines in 2015?” The goal is to essentially use sentiment analysis on Twitter data to get insight into the people’s opinions on US airlines. Central to sentiment analysis are techniques first developed in text mining. Some of those techniques require a large collection of classified text data often divided into two types of data, a training data set and a testing data set. The training data set is further divided into data used solely for the purpose of building the model and data used for validating the model. The process of building a model is iterative, with the model being successively refined until an acceptable performance is achieved. The model is then used on the testing data in order to calculate its performance characteristics.

Sentiment Analysis is a branch of Natural Language Processing (NLP) that allows us to determine algorithmically whether a statement or document is “positive” or “negative”.Sentiment analysis is a technology of increasing importance in the modern society as it allowsindividuals and organizations to detect trends in public opinion by analyzing social mediacontent. Keeping abreast of socio-political developments is especially important during periodsof policy shifts such as election years, when both electoral candidates and companies can benefitfrom sentiment analysis by making appropriate changes to their campaigning and businessstrategies respectively.The purpose of this assignment is to compute the sentiment of text information -in our case, tweets posted in 2019 Canadian elections -and answer the research question: “Whatcan public opinion on Twitter tell us about the Canadian political landscape in 2019?” The goal is to essentially use sentiment analysis on Twitter data to get insight into the 2019 Canadian elections.Central to sentiment analysis are techniques first developed in text mining. Some of those techniques require a large collection of classified text data often divided into two types of data, a training data set and a testing data set. The training data set is further divided into data used solely for the purpose of building the model and data used for validating the model. The process of building a model is iterative, with the model being successively refined until an acceptable performance is achieved. The model is then used on the testing data in order to calculate its performance characteristics.