SESSION

Neuro-Symbolic AI for Sentiment Analysis

Slides PDF Video

Learn to supercharge sentiment analysis with neural networks and graphs. Neural networks are great at automated black-box pattern recognition, graphs at encoding and human-readable logic. Neuro-symbolic computing promises to leverage the best of both.

In this session, you will see how to combine an off-the-shelf neuro-symbolic algorithm, word2vec, with a neural network (Convolutional Neural Network, or CNN) and a symbolic graph, both added to the neuro-symbolic pipeline. The result is an all-Apache Spark text sentiment analysis more accurate than either neural alone or symbolic alone.

Although the presentation will be highly technical, high-level concepts and data flows will be highlighted and visually explained for the more casual attendees. Technologies used include MLlib, GraphX, and mCNN (from spark-packages.org) will be highlighted and visually explained for the more casual attendees.

Technologies used: MLlib, GraphX, and mCNN (from spark-packages.org)

Session hashtag: #SFr12

Michael Malak,  at Oracle

About Michael

Michael Malak is the lead author of Spark GraphX In Action and has been developing Spark solutions at two Fortune 200 companies since early 2013. He has been programming computers since before they could be bought pre-assembled in stores.