Spark streaming application that will perform sentiment analysis using NLP and Kafka. Sentiments are visualized using Kibana, Elasticsearch and Logstash.
Description:
Developed a spark streaming application that will continuously read data from twitter about a topic.
The tweets obtained are analyzed for sentiment using NLP and sent to a Kafka topic.
Sentiments from the Kafka topic were forwarded to Elasticsearch using Logstash as pipeline.
Visualized the sentiment of tweets from the Elasticsearch data using Kibana.
Instructions to run:
It needs 7 arguments to be passed. 4 arguments for twitter to generate oAuth credentials
Arguments to be passed: consumer key, consumer secret key, access token, secret access token,
twitter topic, kafka topic, checkpoint directory path
Run zookeeper, kafka server and create a topic.
Start a consumer to that will dump messages to standard output
Run Elasticsearch, kibana and logstash to visulaize data in real time.
To run the project, run sparkStreaming.scala class on IntelliJ or create an assembly fat jar file and run the jar file with the below command:
spark-submit —packages org.apache.spark:spark-sql-kafka-0-10_2.11:2.4.0
—class ClassName(sparkStreaming) PathToJarFile — 6 arguments