This post was originally published on Kenny Bastani’s blog.

Sentiment analysis uses natural language processing to extract features of a text that relate to subjective information found in source materials.

Movie Review Sentiment Analysis


A movie review website allows users to submit reviews describing what they either liked or disliked about a particular movie. Being able to mine these reviews and generate valuable metadata that describes its content provides an opportunity to understand the general sentiment around that movie in a democratized way.

That’s a pretty cool thing if you think about it. Using machine learning we can democratize subjectivity about anything in the world. We can make an objective analysis of subjective content, giving us the ability to better understand trends around products and services that we can use to make better decisions as consumers.

Sentiment Analysis Data Model


One of the major barriers to unlocking this ability is in the way we structure and transform our data.

The current state-of-the-art methods include approaches such as Naive Bayes, Support Vector Machines and Maximum Entropy. The challenges imposed by these approaches still remains in how features are extracted from a text and structured as data in a way that is the least costly in terms of performance. I decided to focus on solving the problem of performance, in the way features are selected and extracted and the availability of that data as the number of features grow over time.

Using a feature selection algorithm I describe here, I used Neo4j to solve the challenge of data transformation and availability.

While the state of the art natural language parsing algorithms are focused on sentence structure, I’ve decided to pursue a statistical approach to natural language grammar induction. My approach focuses on generalizations across a vast corpus of text, generating new features using deep learning to predict features with the highest probability of being present to the left or right of a new feature.

Graph-Based NLP Example


Let’s assume that the phrase “one of the worst” has been extracted as a feature of a set of texts.

The reason that this phrase was extracted was that a phrase that it was descended from had determined that this particular phrase was the most statistically relevant, meaning that the phrase had the best chance of being matched after the parent phrase.

Using Neo4j we can determine the line of inheritance that produced this phrase as a feature.



Starting at the root node, which is captioned as “{0} {1}”, the path in which the phrase “one of the worst” will be parsed is (the)->(of the)->(one of the)->(one of the worst).

The hierarchy reveals more possibilities as you move deeper from “one of the worst”. Expanding the path seen in the image above to include all possible features that descend from the phrase “one of the worst” reveals the following:



This feature selection algorithm can select on the most statistically relevant features and phrases extracted from a corpus of text in less than a second. The reason an approach like this is extremely relevant to sentiment analysis is that these pattern nodes can be connected to the label of the text they were trained from, as seen below.



The result of this algorithm – and largely thanks to Neo4j’s graph traversals – is that any natural language text can be parsed with sub-second performance and generate a subgraph to be used for whichever classification algorithm makes the most sense for your dataset.

Open Source Demo


For the movie review example I took 500 movie reviews for both negative and positive labels and trained a natural language parsing model in Neo4j using Graphify.

In the next blog post in this series I will introduce you to a demo that can perform better at classifying movie reviews than a human. The human classification error being 0.3, or 70% success.

If you’re looking to get your feet wet before then, take a look at the finished demo here: Graphify Sentiment Analysis for Movie Reviews

Notes


Neo4j is an open source graph database.

Graphify is an open source extension to Neo4j that extends Neo4j to include classification-based algorithms for natural language processing.


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About the Author

Kenny Bastani , Developer Relations

Kenny Bastani Image

Kenny Bastani is a passionate technology evangelist and and open source software advocate in Silicon Valley. As an enterprise software consultant he has applied a diverse set of skills needed for projects requiring a full stack web developer in agile mode.

As a passionate advocate for the popular graph database Neo4j, Kenny has supported developers from globally recognized companies who have inserted the NoSQL database inside their technology stack. As a passionate blogger and open source contributor, Kenny engages a community of passionate developers who are looking to take advantage of newer graph processing techniques to analyze data.


3 Comments

Satrio says:

Hi, I want to ask something regarding the natural language parsing model. In creating the model from 500 movies reviews, does the parsing process means that you break down all text (sentences) into words and then you create each word as a base node and the next node as the base node + the adjacent word available on the corpus? What is the meaning of {0} and {1} ? Thanks

Alex says:

Hello,

It is confusing a little. So, neo4j is a tool to put results of NLP algorithms in a graph or it is an algorithm itself?

rachana says:

what is accuracy of this system?? can u give me a code for my academic research reference??

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