The coding has been done on Python 3.65 using Jupyter Notebook. This program fetches LIVE data from TWITTER using Tweepy. Then we clean our data or tweets ( like removing special characters ). After that we perform sentiment analysis on the twitter data and plot it for better visualization. The we fetch the STOCK PRICE from yahoo.finance and add it to the data-set to perform prediction. We apply many machine learning algorithms like (random forest, MLPClassifier, logistic regression) and train our data-set. Then we perform prediction on untrained data and plot it with the real data and see the accuracy.

Live Twitter sentiment analysis using Python, Apache Spark Streaming, Kafka, NLTK, SocketIO

Sentiment Analysis and Data Visualization

Twitter Live Stream Sentiment Analysis

Live streaming and graphing of tweets from twitter streamed by the tags given by the user

In this assignment I am finding out peoples sentiment about DeMonetisation happened in india. Demonetization means the old unit of currency must be retired and replaced with a new currency unit. This is currently trending issue in India and people have both positive and negative sentiments about. I am collecting tweets which has #DeMonetisation using search API of twitter and classifying them as Positive and negative sentiment based on train data which will train model.In cluster.py i am finding out users who has tweeted about this issue and using there id am also Collecting whom they follow, using this information i am creating graph which will be one big component. This graph i am dividing in different communities using girvan newman. This information i am using to find out average user per community and sentiments of that community. In classify.py I am finding out positive and negative sentiments of people from tweets collected. To find out positive and negative sentiments of people, first i am training my model using train data which Is classifies using AFINN. Then i am collecting live twitter data which will be my test data, and then i am classifying this data as positive and negative sentiments. In summarize.py file i am showing final analysis. By looking at data in file we can say that more people having negative sentiments about DeMonetisation issue and people unhappy about the government decision while there are some people which are happy about government decision and having positive sentiment.

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A Twitter bot that uses Azure's AI to perform sentiment analysis of tweets live. If a tweet is determined to be positive it likes and retweets the tweet. The goal is to reinforce positive online behavior. This is an example of how to make AI work for us instead of just for marketers and other entities that have motivations beyond what is best for humanity.

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Live sentiment analysis of tweets using Kafka

Sentiment analysis on live twitter stream and plotting the sentiment values using Matplotlib

The is a web app for twitter sentiment analysis.

twitter sentiment analysis with/without mood

A web application for determining the sentiment labels of live tweets

Sentiment analysis on streaming data of twitter (tweepy api), deep learning Keras.

Live cognitive Twitter sentiment analysis using IBM Watson. https://live-twitter-sentiment.herokuapp.com/

In this tutorial, we are performing sentiment analysis on live tweets pulled from Twitter via API using Python Tweepy. We are applying Python Library TextBlob & Natural Language Toolkit, viz, NLTK Sentiment Vader SentimentIntensityAnalyzer for Sentiment Computations.

Scrape tweets in Python without the official Twitter (X.com) API and perform a live sentiment analysis. In this example, we'll get the chance to analyse the latest 100 tweets from Elon Musk.

Sentiment analysis of live Twitter Stream

Graphing Live Twitter Sentiment Analysis with NLTK.

A Visualisation Tool in Django for Sentiment Topic Analysis from a live twitter stream

A Web-App deployed on Heroku for live Twitter Sentiment Analysis

Live Twitter sentiment analysis using Python, Apache Spark Streaming, Kafka, NLTK, SocketIO

Live twitter sentiment analysis

Live Twitter data Sentiment Analysis with Streamlit....

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A real-time sentiment analysis tool for tweets, built without using the Twitter API. Tweets are scraped directly from Twitter, analyzed using a lightweight LLaMA model via Croq, and displayed in a live GUI built with Tkinter.

An investment strategy using Twitter sentiment analysis to guide trading decisions. The project processes live tweets, analyzes sentiment trends with NLP, and integrates them with stock data to predict performance. Features data preprocessing, machine learning models, and a backtesting framework for evaluating profitability.

personal project to pull live Twitter data using Nifi getTwitter processor and pushes to Kafka topic which is then consumed by a Spark Streaming application where basic sentiment analysis is performed and the final result is stored in elastic search for visualization using Kibana.