CoreNLP – Sentiment Analysis

by Open Source in on January 5, 2018

Sentiment Analysis

This algo will analyze text to determine the overall sentiment of the content. This can be very useful for analyzing reviews for positive/negative bias and for inputs to content recommendation systems. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.

The underlying technology of this algorithm is based on a new type of Recursive Neural Network that builds on top of grammatical structures. You can also browse the Stanford Sentiment Treebank, the dataset on which this model was trained. The model and dataset are described in an upcoming EMNLP paper. Of course, no model is perfect. You can help the model learn even more by labeling sentences we think would help the model or those you try in the live demo.

How to Run

Docker is the simplest way to get started with this model. We have created a pre-built docker image that includes all dependencies needed to run the Stanford CoreNLP library.

Be sure you have docker installed!

Start the container Be sure to use the -p parameter to ensure you\'ve mapped your local port to the container\'s internal http port 9000

docker run -p 9000:9000 algohub/corenlp

Once the container is started, you can post any text data to port 9000 to get the result.

localhost:9000/?properties={annotators: sentiment, outputFormat: json}

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  • Released
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    January 5, 2018

  • Last Updated
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    June 1, 2020

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