Sentiment Analysis Using SnapLogic Data Science

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Overview

What does this use case do?

This use case demonstrates how you can use SnapLogic Machine Learning (ML) Snaps to perform sentiment analysis. Sentiment analysis enables you to computationally identify and classify opinions expressed in a piece of text.

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This process helps you determine the writer's attitudes towards a specific product, service, or topic, categorizing them as positive, negative, or neutral.

Sentiment analysis enables you to:

  1. Evaluate your customers' satisfaction levels with your product or service. You can do so by applying sentiment analysis to customer-support call records, support tickets, survey data, and so on.
  2. Detect fraud reviews by running review comments through a sentiment analysis system. Typically, the more the number of stars in a review, the more positive the sentiment in the feedback text. In a world where reviews can make or break your business, sentiment analysis is a powerful tool for detecting fake reviews, enabling you to identify reviews where the sentiment expressed in the review text doesn't match the number of stars received.
  3. Understand people based on social media data. Sentiment analysis can be applied to the data scraped from social networking web sites, such as Twitter, Facebook, Instagram, and so on. For example, we can identify promoters and detractors of a movie star based on the sentiments expressed in the comments on his/her Instagram.
In this use case, we build a simple sentiment analysis model that classifies input text as either positive, negative, or neutral in sentiment.

How this use case is structured

In the initial sections of this use case, we offer a high-level description of the project and its key tasks. We then describe the pipelines and Snaps that make up the use case. In each of these sections too, we first offer a functional description of what we are doing before getting into the technical details.

The Dataset Used for this Use Case

For this use case, we use the Yelp dataset. Yelp provides a subset of their data as an open dataset. This dataset contains data about businesses, reviews, users, check-ins, tips and photos. The full dataset is available here.

In this use case, we focus only on user-review data. To simplify our use case, we use only 5-star and 1-star reviews as positive and negative examples, respectively.

Building a Sentiment Analysis Model

Process Summary

In this use case, we perform the following high-level tasks to create a sentiment analysis model:

High-Level Task Description

To build a sentiment analysis model, we perform the following tasks:

  1. Data Preparation: Prepare the data required to train the model.
  2. Cross Validation: Use multiple algorithms to cross-validate the data and identify the algorithm that offers the most reliable results.
  3. Model Building: Build the sentiment analysis model using the algorithm identified in the previous step.
  4. Model Hosting: Make the model available as an ultra-task.
  5. API Testing: Run a sample sentiment analysis request to check whether it works as expected.

Pipelines Used

We use the following pipelines to perform each of the tasks listed above:


PipelineDescription


Data Preparation:

  1. This pipeline reads the dataset from Yelp and, because we are sure that reviews associated with a 1-star rating are definitely negative and those associated with a 5-star rating are definitely positive, retains only the 1-star and 5-star reviews. Doing so enables us to build a reliable model.
  2. The pipeline now uses stratified sampling to balance the ratio of 1-star and 5-star reviews. This ensures that our model is not biased towards either sentiment.
  3. Once we have a balanced dataset, we break each review into an array of words and identify the 200 words that are most commonly used across reviews. These are the words whose relative placement to each other we shall examine to create our algorithm.
  4. Finally, we generate statistics (such as the most popular word, the number of unique words, and so on) related to these 200 words to better understand how they function in the sample dataset.

Cross ValidationWe have 2 pipelines in this step. The top pipeline (child pipeline) performs k-fold cross validation using a specific ML algorithm. The pipeline at the bottom (the parent pipeline) uses the Pipeline Execute Snap to automate the process of performing k-fold cross validation on multiple algorithms.

  1. The Pipeline Execute Snap spawns and executes the child pipeline multiple times with different algorithms. You can execute instances of the child pipeline sequentially or leverage the SnapLogic multi-core processor to speed up the process by executing these pipelines in parallel.

  2. The Aggregate Snap applies the max function to identify the algorithm that offers the best results.

Model Building. Based on the cross validation result, we can see that the logistic regression and support vector machines algorithms perform the best.

  1. The Trainer (Classification) Snap trains the logistic regression model.
  2. Once the model is ready, we use the JSON Formatter and File Writer Snaps to write the model to SLFS.

Model HostingThis pipeline is scheduled as an Ultra Task to offer sentiment analysis as a REST-API-driven service to external applications.

  1. New requests are offered as input to the Filter Snap.
  2. The Predictor (Classification) Snap receives the the ML model from the JSON Parser1 Snap and hosts it.
  3. The Tokenizer and Bag of Words Snaps prepare the input text for sentiment analysis.
  4. The File Reader Snap reads the common words that are required for the bag of words operation.

For more information on how to offer an ML ultra task as a REST API, see SnapLogic Pipeline Configuration as a REST API.

API Testing. This pipeline takes a sample request, sends it as a REST API request, and displays the results received.

  1. The JSON Generator Snap contains a sample request, including the token and the text.
  2. The REST Post Snap sends a request to the Ultra Task (API).
  3. The Mapper Snap extracts prediction from the response body. More information on scheduling and managing ML ultra tasks is here.

The Data Preparation Pipeline

We design the Data Preparation pipeline as shown below:

This is where it gets technical. We have highlighted the key Snaps in the image below to simplify understanding.


This pipeline contains the following key Snaps:


Snap LabelSnap NameDescription
1Read Review DatasetFile ReaderReads an extract of the Yelp dataset containing 10,000 reviews from the SnapLogic File System (SLFS).
2Filter 1 and 5 StarsFilterRetains only 1-star and 5-star reviews.
3Stratified SamplingSampleApplies stratified sampling to balance the ratio of 1-star and 5-star reviews.
4Extract Text and SentimentMapperMaps $stars to $sentiment and replaces 1 (star) with negative, and 5 (star) with positive. It also allows the input data ($text) to pass through unchanged to the downstream Snap.
5TokenizerTokenizerBreaks each review into an array of words, of which two copies are made.
6Common WordsCommon WordsComputes the frequency of the top 200 most common words in one copy of the array of words.
7Bag of WordsBag of WordsConverts the second copy of the array of words into a vector of word frequencies.
8ProfileProfileComputes data statistics using the output from the Common Words Snap.

Key Data Preparation Snaps

Filter

This Snap keeps 1-star and 5-star reviews while dropping all others (2-star, 3-star, and 4-star reviews). This brings the number of useful reviews down from 10000 to 4428.

 Output preview

The output contains only 1-star and 5-star reviews, as shown in the screen below.

Sample

In the filtered list of 4428 reviews, there are 3555 5-star reviews and 873 1-star reviews. If we train the ML model on this dataset, the model will be biased towards positive (5-star) reviews, because it will get to sample more positive reviews than negative ones. This is what we call an unbalanced problem. There are many methods of dealing with unbalanced datasets, including downsampling (reducing the number of samples that form the majority until a balance is reached) and upsampling (duplicating samples that form the minority). In this case, we use the downsampling method.

We keep all the 873 1-star reviews and the same number of 5-star reviews. The Pass through percentage is (873 + 873) / 4428 = 0.3943. We specify $stars in the Stratified field property and select Stratified Sampling as the Algorithm. Stratified sampling is a technique that maintains the same number of documents of each class; in this case, the class is $stars.

 Output preview

The output contains fewer documents; however, the list contains an equal number of 1-star and 5-star reviews.

Mapper

This Snap maps $stars to $sentiment and allows the input data ($text) to pass through. Moreover, in the $sentiment field, 1 (star) is replaced by negative, and 5 (star) is replaced by positive.

 Output preview

The output contains $sentiment and $text. The $sentiment field is derived from $stars: 1-star is negative, while 5-star is positive. $text passes through unchanged, and is the same as in the original dataset.

Tokenizer

Training ML models on text data can be complicated and ineffective, since most of the generic ML algorithms prefer numeric data. We use the Tokenizer, Common Words, and Bag of Words Snaps to convert text into numbers. First, the Tokenizer Snap breaks the input sentences into a single array of words (tokens). For the purposes of this use case, we are assuming that special characters do not impact the sentiment expressed; so, we select the Word only check box to remove all special characters from the input text.

 Output preview

The output contains $text as an array of words (tokens).

Common Words

In order to perform a bag of words operation, we need to specify the words (bags) that we want to include. In most cases, we use the most common words in the dataset. This we do in this pipeline using the Common Words Snap, and retain the 200 most-common words in the dataset. We also output the frequency of each word.

 Output preview

The output contains the 200 most common words in the dataset along with their frequency.

This output is the result of the pipeline validation performed on the first 50 documents in the dataset. The result based on the full dataset can be generated only when the pipeline is executed.

Bag of Words

This Snap receives a list of common words from the Common Words Snap and computes the frequency of each word in the input document.

 Output preview

The output contains a new field for each common word. Since we set the Top word limit in the Common Words Snap to 200, the output of this Snap contains 201 fields ($sentiment + 200 fields for 200 common words). The number is the frequency. For example, the $text in the first input document contains 4 instances of the, 3 of and, 0 of to, 4 of is, and so on.

This output is the result of the pipeline validation performed on the first 50 documents in the dataset. The result based on the full dataset can be generated only when the pipeline is executed.

Profile

This Snap computes data statistics of the dataset. You can see the output of this Snap here.

 Output preview

The output contains data statistics of this dataset (after performing the bag of words operation). $sentiment field is categorical, so binning does not apply, and the data statistics do not include mean, min, max, and standard