Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis

Let's perform Twitter sentiment analysis using Python, Docker, Elasticsearch, and Kibana!

Twitter sentiment analysis using Spark and Stanford CoreNLP and visualization using elasticsearch and kibana

An example of Twitter realtime analysis with Kubernetes, Flink, Kafka, Kafka Connect, Cassandra, Elasticsearch/Kibana, Docker, Sentiment Analysis, Xgboost and Websockets

Twitter sentiment analysis using Python, Elasticsearch, and Kibana!

StockInsight predicts the stock prices using Neural Networks. It uses Elasticsearch, Twitter data, News Headlines and sentiment analysis to find the effect of emotions on the stock price. How much do emotions on twitter and news headlines affect stock price? What could be the price of stock within next 30 minutes?

Web Application that allows users to search keywords from an AWS Elasticsearch Service powered by AWS Elastic Beanstalk. The Twitter Streaming API is used to send tweets to AWS SQS which is then handled by a worker that judges a tweet's sentiment or whether tweets are positive, negative, or neutral - this leverages IBM's Natural Language Understanding API. Once a sentiment is obtained, tweets are pushed to AWS SNS service which then are pushed to services that are interested in this topic. User may ask an HTTP/HTTPS, email, or SMS endpoint to subscribe to this AWS SNS topic. In this case, AWS Elasticsearch is the endpoint that is subscribed to the SNS topic. A web interface is provided to view tweets or filter them. Viewers may read the tweet and its corresponding sentiment by hovering over them on Google Map.

Sentiment Analysis of Tweets with Elasticsearch using Twitter API

Twitter Sentiment Analysis using Elasticsearch

Twitter Sentiment Analysis using FastText, Elasticsearch, Kibana

Performing a sentiment analysis of twitter data using hashtags, then cleaning and collecting tweets of a specific region; used google and twitter apis in the process and then used kibana to visualize data along with elasticsearch

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Sentiment analysis of Twitter posts using python, elasticsearch and kibana

Created a Spark Streaming Application that collects streaming data from Twitter about a topic. The Twitter feeds are then analyzed for their sentiment using Elasticsearch and visualized using Kibana. We can see the trend of sentiment as positive or negative.

StockInsight predicts the stock prices using Neural Networks. It uses Elasticsearch, Twitter data, News Headlines and sentiment analysis to find the effect of emotions on the stock price.

Stock market analyzer and stock predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis. How much do emotions on Twitter and news headlines affect a stock's price? Let's find out.....

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

A Spark Streaming application that will continuously read data from Twitter about a topic. These Twitter feeds will be analyzed for their sentiment, and then analyzed using ElasticSearch. To exchange data between these two, you will use Kafka as a broker. Everything will be done locally on your computer

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Twitter Sentiment Analysis using Akka, Apache Spark and Elasticsearch

Twitter Sentiment Analysis with Spark, SparkStreaming, Kafka, Elasticsearch & Kibana

Twitter-Sentiment-Analytics-using-Apache-Spark-Streaming-Kafka-elasticsearch-Python

twitter sentiment analysis running on Docker using ElasticSearch and Kibana

Twitter geolocation and sentiment analysis with PySpark, Elasticsearch, Kibana Dashboard

Sentimental Analysis for Twitter in Python

example of ingestion using twitter and sentiment analysis for Elasticsearch

Twitter Sentiment Analysis using kafka ,kibana,elasticSearch,Spark Streaming in real time

Scrape Twitter by keyword, store in Elasticsearch and perform sentiment analysis (eventually)

Performing sentiment analysis of a given hashtag using Apache Kafka, TextBlob, Twitter API, Elasticsearch and Kibana.

Send Twitter data into Spark Streaming and run Sentiment Analysis then plot the result using ElasticSearch Kibana