GCP AppEngine project of Twitter data and some sample code

A simple python script to receive all links from tweets on a topic you want to follow. This is with and without hashtags and @ mentions,and inclusive of every links shared within that tweet, all Live stream.

Algorithms For Automatic Labelling Of Text Data. A Text Mining project that studies Supervised and Semi-Supervised Learning on Twitter data.

Collected tweets using Twitter API tweepy, pre-processed the tweets using R, to get rid of the biased users. Calculated the sentiments score of each user in R and python and observe the trends and the differences in results of python and R. Using python, scikit-learn, R, ggplot and e1071 libraries.

{rtweet} helpers for automating large or time-consuming downloads

A web app that helps you discover nearby places of interest, and also displays the latest coronavirus related tweets with the place's name in the tweets

A Batch Processing Data pipeline to ingest the twitter data for a particular hashtag using twitter API’s into a csv file in batches into Hadoop filesystem.

Worked with Twitter data to analyze Donald Trump's tweets using sentiment analysis

Sentiment analysis and text summarization pipeline framework.

Text Mining Analysis of Twitter Data

we are building a sentiment classifier, which will detect how positive or negative each tweet is.

Twitter Sentiment Analysis Model using NLP

Bitcoin Sentiment Analysis

Stand-alone PHP CLI script to batch/post-process downloaded full twitter backup-archive files

Conference presentation materials

Social Media Mining on Twitter Data

Tweets related to Revocation of Article 370 and 35A from Indian Constitution were collected and analysed.

An application to analyze sentiments of a particular topic using Spark and real-time Twitter feeds (i.e. positive, negative, and neutral). Elasticsearch and Kibana were utilized for storage and visualization respectively

Built friendship graph from Twitter data and performed community detection.