This project leverages R, Shiny, and Markdown to analyze sentiments from tweets about Delta Airlines. It includes data collection via the Twitter API, sentiment analysis, and interactive dashboards.

Basic twitter sentiment analytics is performed using Apache Spark Streaming API's. Real time tweets data stream is processed. Apace Kafka is used as queuing service for data streams.

Crypto Collective aggregates tweets accessed through the Twitter API. It then processes these tweets using Microsoft Azure's text analytics service which returns numerical user sentiment data. This data was then formatted into a user friendly interface and displayed on our website. This will undoubtedly aid users in their cryptocurrency investments.

Twitter Data Pipeline Using Airflow – Built an end-to-end automated data pipeline using Apache Airflow, Twitter API, and Python for real-time tweet extraction, transformation, and sentiment analysis. Deployed on AWS EC2 with storage in Amazon S3, enabling real-time analytics and dashboards.

I was trying to get along with Sentimental Analysis on Twitter data based on the CORONAVIRUS tweets. There is a very helpful API provided by Twitter for those who want to dive into the field of Social Data Analytics and know how people express their sentiments through Social media. I have used different packages like NLTK, Spacy , Vader to compare different types of words or sentences and get the polarities. Also, this analysis shows how accurate Lemmatization is compared to Stemming and why it should be preferred.

Basic twitter sentiment analytics is performed using Apache Spark Streaming API's. Real time tweets data stream is processed. Apace Kafka is used as queuing service for data streams.

This Twitter data scraping project automates the extraction of tweets and user information using the Twitter API. It allows for real-time data collection, sentiment analysis, and trend tracking to support research and analytics.

In this project, basic twitter sentiment analytics is performed using Apache Spark Streaming API's. Real time tweets data stream is processed. Apace Kafka is used as queuing service for data streams.

The Twitter API was employed to gather tweets related to plastic pollution, and these tweets underwent analysis using a Natural Language Processing (NLP) model to ascertain public sentiments concerning both plastic waste and manufacturing. The resulting dataset was then segmented and utilized for a comprehensive data analytics .

Cloud Computing and Big Data (SWE485) course project that aims to analyze the sentiments about Riyadh Season-2019, specifically about Hittin neighborhood by retrieving the public's tweets from Twitter API using Python. Logistic Regression and Naive Bayes models are applied, following the data analytics lifecycle. The results are visualized using Plotly and Seaborn libraries.