State of the Art Natural Language Processing
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Updated
Jun 13, 2024 - Scala
State of the Art Natural Language Processing
An example of Twitter realtime analysis with Kubernetes, Flink, Kafka, Kafka Connect, Cassandra, Elasticsearch/Kibana, Docker, Sentiment Analysis, Xgboost and Websockets
Sentiment analysis using VADER in Scala
Streaming Twitter Sentiment Analysis with Apache Spark
Financial Forecasting and its correlation with Human Sentiments using Distributed Computing on Spark Framework
Classifies whether a particular tweet has positive or negative sentiment
Simple show case of consuming twitter api and performing sentiment analysis over tweets
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.
Real time twitter streams analysis
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
A content moderating system for social media feeds
data pipeline using the lambda architecture is created for the unification and consolidation of real-time customer web events, weblogs, and profile data into a hive warehouse for adhoc analysis.
Tweets sentiment analysis in Spark
PredictionIO sentiment analysis engine using OpenNLP
Spark NLP sentiment analysis applied on Spark Structured streaming
Sentiment Analysis project on tweets.
Twitter Sentiment Analyzing using Scala and Spark
This is the CSYE7200 FinalProject for Team2 Spring2017
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