BigQuery ML for Sentiment Analysis: How to Make the Most of Your Data
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Introduction
We recently announced BigQuery support for sparse features which help users to store and process the sparse features efficiently while working with them. That functionality enables users to represent sparse tensors and train machine learning models directly in the BigQuery environment. Being able to represent sparse tensors is a useful feature because sparse tensors are used extensively in encoding schemes like TF-IDF as part of data pre-processing in NLP applications and for pre-processing images with a lot of dark pixels in computer vision applications.
There are numerous applications of sparse features such as text generation and sentiment analysis. In this blog, we’ll demonstrate how to perform sentiment analysis with the space features in BigQuery ML by training and inferencing machine learning models using a public dataset. This blog also highlights how easy it is to work with unstructured text data on BigQuery, an environment traditionally used for structured data.
Using sample IMDb dataset
Let’s say you want to conduct a sentiment analysis on movie reviews from the IMDb website. For the benefit of readers who want to follow along, we will be using the IMDb reviews dataset from BigQuery public datasets. Let’s look at the top 2 rows of the dataset.
Although the reviews table has 7 columns, we only use reviews and label columns to perform sentiment analysis for this case. Also, we are only considering negative and positive values in the label columns. The following query can be used to select only the required information from the dataset.
SELECT
review,
label,
FROM
`bigquery-public-data.imdb.reviews`
WHERE
label IN ('Negative', 'Positive')The top 2 rows of the result is as follows:
Methodology
Based on the dataset that we have, the following steps will be carried out:
- Build a vocabulary list using the review column
- Convert the review column into sparse tensors
- Train a classification model using the sparse tensors to predict the label (“positive” or “negative”)
- Make predictions on new test data to classify reviews as positive or negative.
Feature engineering
In this section, we will convert the text from the reviews column to numerical features so that we can feed them into a machine learning model. One of the ways is the bag-of-words approach where we build a vocabulary using the words from the reviews and select the most common words to build numerical features for model training. But first, we must extract the words from each review. The following code creates a dataset and a table with row numbers and extracted words from reviews.
-- Create a dataset named `sparse_features_demo` if doesn’t exist
CREATE SCHEMA IF NOT EXISTS sparse_features_demo;
-- Select unique reviews with only negative and positive labels
CREATE OR REPLACE TABLE sparse_features_demo.processed_reviews AS (
SELECT
ROW_NUMBER() OVER () AS review_number,
review,
REGEXP_EXTRACT_ALL(LOWER(review), '[a-z]{2,}') AS words,
label,
split
FROM (
SELECT
DISTINCT review,
label,
split
FROM
`bigquery-public-data.imdb.reviews`
WHERE
label IN ('Negative', 'Positive')
)
);The output table from the query above should look like this:
The next step is to build a vocabulary using the extracted words. The following code creates a vocabulary including word frequency and word index from reviews. For this case, we are going to select only the top 20,000 words to reduce the computation time.
-- Create a vocabulary using train dataset and select only top 20,000 words based on frequency
CREATE OR REPLACE TABLE sparse_features_demo.vocabulary AS (
SELECT
word,
word_frequency,
word_index
FROM (
SELECT
word,
word_frequency,
ROW_NUMBER() OVER (ORDER BY word_frequency DESC) - 1 AS word_index
FROM (
SELECT
word,
COUNT(word) AS word_frequency
FROM
sparse_features_demo.processed_reviews,
UNNEST(words) AS word
WHERE
split = "train"
GROUP BY
word
)
)
WHERE
word_index < 20000 # Select top 20,000 words based on word count
);The following shows the top 10 words based on frequency and their respective index from the resulting table of the query above.
Creating a sparse feature
Now we will use the newly added feature to create a sparse feature in BigQuery. For this case, we aggregate word_index and word_frequency in each review, which generates a column as ARRAY[STRUCT] type. Now, each review is represented as ARRAY[(word_index, word_frequency)].
-- Generate a sparse feature by aggregating word_index and word_frequency in each review.
CREATE OR REPLACE TABLE sparse_features_demo.sparse_feature AS (
SELECT
review_number,
review,
ARRAY_AGG(STRUCT(word_index, word_frequency)) AS feature,
label,
split
FROM (
SELECT
DISTINCT review_number,
review,
word,
label,
split
FROM
sparse_features_demo.processed_reviews,
UNNEST(words) AS word
WHERE
word IN (SELECT word FROM sparse_features_demo.vocabulary)
) AS word_list
LEFT JOIN
sparse_features_demo.vocabulary AS topk_words
ON
word_list.word = topk_words.word
GROUP BY
review_number,
review,
label,
split
);Once the query is executed, a sparse feature named `feature` will be created. That `feature` column is an `ARRAY of STRUCT` column which is made of `word_index` and `word_frequency` columns. The picture below displays the resulting table at a glance.
Training a BigQuery ML model
We just created a dataset with a sparse feature in BigQuery. Let’s see how we can use that dataset to train with a machine learning model with BigQuery ML. In the following query, we will train a logistic regression model using the review_number, review, and feature to predict the label:
-- Train a logistic regression classifier using the data with sparse feature
CREATE OR REPLACE MODEL sparse_features_demo.logistic_reg_classifier
TRANSFORM (
* EXCEPT (
review_number,
review
)
)
OPTIONS(
MODEL_TYPE='LOGISTIC_REG',
INPUT_LABEL_COLS = ['label']
) AS
SELECT
review_number,
review,
feature,
label
FROM
sparse_features_demo.sparse_feature
WHERE
split = "train"
;Now that we have trained a BigQuery ML Model using a sparse feature, we evaluate the model and tune it as needed.
-- Evaluate the trained logistic regression classifier
SELECT * FROM ML.EVALUATE(MODEL sparse_features_demo.logistic_reg_classifier);The score looks like a decent starting point, so let’s go ahead and test the model with the test dataset.
-- Evaluate the trained logistic regression classifier using test data
SELECT * FROM ML.EVALUATE(MODEL sparse_features_demo.logistic_reg_classifier,
(
SELECT
review_number,
review,
feature,
label
FROM
sparse_features_demo.sparse_feature
WHERE
split = "test"
)
);
The model performance for the test dataset looks satisfactory and it can now be used for inference. One thing to note here is that since the model is trained on the numerical features, the model will only accept numeral features as input. Hence, the new reviews have to go through the same transformation steps before they can be used for inference. The next step shows how the transformation can be applied to a user-defined dataset.
Sentiment predictions from the BigQuery ML model
All we have left to do now is to create a user-defined dataset, apply the same transformations to the reviews, and use the user-defined sparse features to perform model inference. It can be achieved using a WITH statement as shown below.
WITH
-- Create a user defined reviews
user_defined_reviews AS (
SELECT
ROW_NUMBER() OVER () AS review_number,
review,
REGEXP_EXTRACT_ALL(LOWER(review), '[a-z]{2,}') AS words
FROM (
SELECT "What a boring movie" AS review UNION ALL
SELECT "I don't like this movie" AS review UNION ALL
SELECT "The best movie ever" AS review
)
),
-- Create a sparse feature from user defined reviews
user_defined_sparse_feature AS (
SELECT
review_number,
review,
ARRAY_AGG(STRUCT(word_index, word_frequency)) AS feature
FROM (
SELECT
DISTINCT review_number,
review,
word
FROM
user_defined_reviews,
UNNEST(words) as word
WHERE
word IN (SELECT word FROM sparse_features_demo.vocabulary)
) AS word_list
LEFT JOIN
sparse_features_demo.vocabulary AS topk_words
ON
word_list.word = topk_words.word
GROUP BY
review_number,
review
)
-- Evaluate the trained model using user defined data
SELECT review, predicted_label FROM ML.PREDICT(MODEL sparse_features_demo.logistic_reg_classifier,
(
SELECT
*
FROM
user_defined_sparse_feature
)
);Here is what you would get for executing the query above:
And that’s it! We just performed a sentiment analysis on the IMDb dataset from a BigQuery Public Dataset using only SQL statements and BigQuery ML. Now that we have demonstrated how sparse features can be used with BigQuery ML models, we can’t wait to see all the amazing projects that you would create by harnessing this functionality.
If you’re just getting started with BigQuery, check out our interactive tutorial to begin exploring.
End Security Risks with the Unattended Projects Recommender Feature
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In fast-moving organizations, it’s not uncommon for cloud resources, including entire projects, to occasionally be forgotten about. Not only such unattended resources can be difficult to identify, but they also tend to create a lot of headaches for product teams down the road, including unnecessary waste and security risks.
To help you prune your idle cloud resources, we’re excited to introduce Unattended Project Recommender. It’s a new feature of Active Assist that provides you with a one-stop shop for discovering, reclaiming, and shutting down unattended projects. With actionable and automatic recommendations, you no longer have to worry about wasting money or mitigating security risks presented by your idle resources. Unattended Project Recommender uses machine learning to identify, with a high degree of confidence, projects that are likely abandoned based on API and networking activity, billing, usage of cloud services, and other signals. This feature is available via the Recommender API today, making it easy for you to integrate with your company’s existing workflow management and communication tools, or export results to a BigQuery table for custom analysis.
Thousands of projects can be unattended in large organizations, presenting major security risks
Your cloud projects can go abandoned or unattended for a number of reasons — ranging from a test environment that’s no longer needed, to project cancellation, to project owner switching jobs, and more. Not only can such projects contribute to your cloud bill (waste) but they may contain security issues such as open firewalls or privileged service account keys that attackers can exploit to get a hold of your cloud resources for cryptocurrency mining or, worse, compromise your company’s sensitive data. These security risks tend to grow over time because the latest best practices and patches are usually not applied to unattended projects.
We experience this issue here at Google, too… In fact, it has been on Google’s internal security team’s radar for some time now, so we joined forces and looked into this problem together, starting with our very own “google.com” organization cloud projects. We quickly found some projects that were unattended, but remediating this issue was easier said than done due to challenges in several areas:
- Detection: With lots of signals available to you via sources like Cloud Monitoring, what are the right ones you should look at (e.g. API, networking, user activity)? How can you tell the difference between an unattended project and a project that has a low level of activity by design (e.g. a “shell” project that holds an auth token)?
- Remediation: Once you have identified a project that seems abandoned, how do you go about ensuring that it’s indeed an unattended project? How do you reduce the risk of deleting something that might be essential to a production workload, causing irreversible data loss? How do you solve this at the scale of your entire organization, beyond a one-time cleanup?
Over the course of 2021 we built and tested a Google-internal prototype first, cleaning up many of our internal unattended projects, and then worked with a number of Google Cloud customers to build and tune this feature based on real-life data (thank you to all of our early adopters for working with us and your generous feedback that helped us shape this feature!) It was not uncommon for us to come across organizations with thousands of unattended projects, and we’re very excited to bring Unattended Project Recommender to all customers, in public preview.
Discovering and acting on unattended project recommendations
Unattended Project Recommender analyzes usage activity across all projects under your organization, including the following data:
- API activity (e.g. service accounts with authentication activity, API calls consumed)
- Networking activity (ingress and egress)
- Billing activity (e.g. services with billable usage)
- User activity (e.g. active project owners)
- Cloud services usage (e.g. number of active VMs, BigQuery jobs, storage requests)
Based on these signals, it can generate recommendations to clean up projects that have low usage activity (where “low usage” is defined using a machine learning model that ranks projects in your organization by level of usage), or recommendations to reclaim projects that have high usage activity but no active project owners. Here’s what an example post-processed summary list of recommendations can look like for the “foobar” organization that has 3 projects:
Project ID: demo-project-307815Recommendation: CLEANUP_PROJECTProject ID: new-projectRecommendation: N/AProject ID: bobs-playground-projectRecommendation: RECLAIM_PROJECT
In addition to the recommendations, you can also examine the underlying project activity insights that the recommendations are based upon. The insights provide additional information that can be useful for integration with your organization’s existing workflows and automation (e.g. send an auto-generated email or chat message to project owners based on the list provided by the owners field). Here’s an example insight payload:
content:activeAppengineInstanceDailyCount: 0activeCloudsqlInstanceDailyCount: 0activeGceInstanceDailyCount: 3activeServiceAccountDailyCount: 1apiClientDailyCount: 18922 // Daily average API calls producedbigqueryInflightJobDailyCount: 0bigqueryInflightQueryDailyCount: 0bigqueryStorageDailyBytes: 0bigqueryTableDailyCount: 0consumedApiDailyCount: 0 // Daily average API calls consumeddatastoreApiDailyCount: 0gcsObjectDailyCount: 11gcsRequestDailyCount: 0gcsStorageDailyBytes: 2663548hasActiveOauthTokens: false // OAuth tokens used in the last 180 dayshasBillingAccount: truenumActiveUserOwners: 1owners: // List of project owners- activeOnProject: falsemember: user:user1@example.com- activeOnProject: truemember: user:user2@example.comserviceWithBillableUsage:– Cloud Storage- Compute EnginevpcEgressDailyBytes: 264456938 // Daily average VPC egress bytesvpcIngressDailyBytes: 392435047 // Daily average VPC ingress bytesusagePercentile: 20 // Level of usage relative to other projects
GCP projects are used in many different ways and for many different purposes. In case you get a recommendation to delete a project that’s being used in a way that’s out of the scope for this feature, you can dismiss the recommendation and it will stop showing up for the given project.
Restoring deleted projects
When you choose to shut down a project using the projects.delete() method, it gets marked for deletion. After a project is marked for deletion, it becomes unusable, all resources within that project are shut down, and a 30-day wait period for the project and all of its data to get fully deleted begins.
In case a useful project is accidentally shut down, you have the option to restore the project within that 30-day wait period. Since restoring allows you to recover most but not necessarily all of your project data and resources, we recommend carefully examining the utilization insights associated with a project and considering any additional utilization signals that may not be captured by the Unattended Project Recommender before taking the cleanup action.
Early customer success stories
A number of enterprise customers are already using Unattended Project Recommender to keep their organizations clean of unattended projects and resources.
Decathlon, a French sporting goods retailer, is excited for the insight Unattended Project Recommender will bring to their environment, and are already deploying it as a part of their latest cloud security initiatives.
“After a thorough test of this feature and the validation of our CISO, we ended up deleting our first 775 projects, and no one complained! A great help to improve our security. The next step for us will be to operationalize it at scale, and implement a company wide policy for unattended resource management.” —Adeline Villette, Cloud Security Officer
For Veolia, one of the world’s largest water, waste and energy management companies, not only does this feature reduce security risks and waste, but also helps drive cultural shift and alignment with its ecological transformation strategy.
“This feature allows us to reduce our costs and security debt on assets that are no longer in use, and is also fully in line with Veolia’s philosophy of limiting its carbon footprint. After having tested Unattended Project Recommender on more than 3,000 projects throughout our organization, we are looking to bring it as proactive alerts to our project owners at scale.”—Thomas Meriadec, Product Manager
Box, a secure cloud content management provider, views it as a foundation for building a repeatable process to remediate unused resources.
“Unattended Project Recommender is a great fit for us. It gives us a unified view of project usage across our entire organization and enables us to address security risks of legacy projects in a systematic and organized manner, ensuring an even safer environment.” —Matt Bowes, Staff Security Engineer
Getting started with the Unattended Project Recommender
To help you get started, we’ve prepared a Cloud Shell tutorial (source code) that you can use to find unattended project recommendations within your own Projects/Folders/Organization. Click this button to clone the tutorial from GitHub and run in your Cloud Shell environment:
As you can see, listing recommendations for your projects only takes a few clicks with the tutorial (special thanks to Lanre Ogunmola, Security & Compliance Specialist, for making this look so easy)! For additional detail on using the gcloud CLI or API to discover unattended project recommendations, please refer to the documentation page.
You can also automatically export all recommendations from your Organization to BigQuery and then investigate the recommendations with DataStudio or Looker, or use Connected Sheets that let you use Google Workspace Sheets to interact with the data stored in BigQuery without having to write SQL queries.
As with any other Recommender, you can choose to opt out of data processing at any time by disabling the appropriate data groups in the Transparency & control tab under Privacy & Security settings.
We hope that you can leverage Unattended Project Recommender to improve your cloud security posture and reduce cost, and can’t wait to hear your feedback and thoughts about this feature! Please feel free to reach us at active-assist-feedback@google.com and we also invite you to sign up for our Active Assist Trusted Tester Group if you would like to get early access to the newest features as they are developed.
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How L&T Financial Services Processes 95% of Motorcycle Loans in Less Than Two Minutes
L&T Financial Services is one of the largest lenders in India. India’s demonetization policy in recent years has led to a shift from cash transactions to digital payments. In 2016, the government withdrew 500 and 1000 rupee notes from circulation and encouraged a heavily cash-based population to deposit their canceled notes in banks. Financial institutions needed to pivot to a new way of doing business to stay competitive. L&T Financial Services modernized its IT infrastructure to keep up with changes and capture digital opportunities.
“Working capital is crucial to stimulate growth in rural communities. Our role as a lender is to provide access to funds. We don’t want to burden borrowers with the complexities of getting a loan. Towards this end, digitization is an important step,” says Dinanath Dubhashi, Managing Director and CEO at L&T Financial Services. “Google Cloud helps us streamline service delivery and identify the right customers. By offering the fastest processing time in the industry, we want to be the go-to lender for all customers.”
L&T Financial Services considered multiple cloud providers before choosing Google Cloud. According to Dinanath, Google Cloud understands both the need for businesses to move fast and the need for IT to modernize at different speeds. “We weren’t forced to abandon existing IT systems and migrate lock, stock, and barrel to Google Cloud on day one.”
L&T Financial Services engaged Google Cloud Professional Services to guide its digital transformation journey. The smooth migration from proof of concept to full-scale deployment on Google Cloud took a matter of months.
“Collaboration: a small idea with big opportunities. G Suite helps us connect remote branches with the head office, easily access shared files to submit and track approvals, and conduct face-to-face discussions to accelerate approval processes.”
—Dinanath Dubhashi, MD and CEO, L&T Financial Services
Digitizing the workforce with G Suite
The move to the cloud at L&T Financial Services started in 2017 when the company introduced G Suite to its 14,500 employees. The legacy email system was cumbersome to use, especially for frontline staff who need email access while they are on the road. Using Gmail, employees can connect with customers and co-workers from anywhere, on any device. Employees save time by scheduling meetings with Calendar, collaborating on Docs, and conducting video calls using Hangouts Meet.
Converting data into credit insights using BigQuery
Taking data intelligence one step further, L&T Financial Services adopts a responsible lending approach by applying algorithm-based data analytics to improve credit standards. Beyond traditional data such as credit score and credit payment history, the company also considers macro-economic indicators for risk audits. For example, a farmer’s ability to pay off the loan of his new tractor depends on a successful planting and harvest. So L&T Financial Services feeds long-term data into BigQuery and runs queries to predict loan defaults based on rainfall and crop yield.
The Future of Language Processing: Google Cloud’s Enhanced NLP Models
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Natural language understanding (NLU) is getting increasingly better at solving complex problems and these language breakthroughs are creating big waves in Artificial Intelligence. For example, new language models are enabling Everyday Robots to create more helpful robots that can break down user instructions and have even enabled people to generate imaginative visuals from complex text prompts.
These leaps in NLU are powered by neural networks trained to understand human language. This technology has greatly advanced since the introduction of Google’s Transformer architecture in 2017 with the introduction of large models trained on massive amounts of data like GPT-3 and, even more recently, with GLaM, LaMDA, and PaLM. This latest generation of models are called Large Language Models (LLMs) because of their sheer size and the vast volumes of data on which they are trained, and they can be applied to a range of tasks to create more powerful digital assistants, generate better search results and product recommendations, enforce smarter platform curation and safety features, and much more.
For these reasons, we’re pleased to announce we’ve updated the Google Cloud Natural Language (NL) API with a new LLM-based model for Content Classification.
With an expansive pre-trained classification taxonomy, the newest version of Content Classification from the Natural Language API leverages the latest Google research to improve customer use cases spanning actionable insights on user trends, to ad targeting, to content-based filtering. In this article, we’ll explore the NL API’s new capabilities, which are the first of many efforts we’ll be making to bring the power of LLMs to Google Cloud.
How LLMs help machines understand human language
As Google Cloud VP and General Manager of AI and Industry Solutions, Andrew Moore has argued, if computer systems become more conversant with natural human languages, they become a foundation for more sophisticated use cases, able to not only understand user intent but also create complex bespoke solutions. Google has been a leading research force in this space, with LLM projects like LaMDA, PaLM and T5 contributing to the Cloud NL API’s improved v2 classification model.
Parsing language is a difficult AI task for machines due in part to the contextual and individual interpretation of words or phrases. The word “server,” for example, could refer to a computer, a restaurant employee, or a tennis player. To understand the word, a model needs to be trained around not only a basic definition but also the context and positioning of the word within a sentence or conversation and its evolving connotations. Because they process voluminous training data via Transformers, LLMs are well-suited to this type of work.
Thanks to the integration of Google’s latest language modeling technology, and an updated and expanded training data set, the next generation of the Content Classification API not only has over 1,000 labels (up from around 600 previously), but now also supports 11 languages (with Chinese, French, German, Italian, Japanese, Korean, Portuguese, Russia, Spanish, and Dutch joining previously-available English)—and does so with improved accuracy.
AI raises questions about the best way to build fairness, interpretability, privacy, and security into these new systems in order to benefit people and society. At Google, we prioritize the responsible development of AI and take steps to offer products where a responsible approach is built in by design. For Content Classification, we limited use of sensitive labels and conducted performance evaluations. See our Responsible AI page for more information about our commitments to responsible innovation.
Get Started
Today’s announcement is just the first step in bringing LLM capabilities to Google Cloud AI products, and we’re excited to see how our more powerful Natural Language API helps developers, analysts and data scientists generate insights and offer superior experiences. Our early adopters are implementing the API to improve user recommendations, display ad targeting, and insights about new trends.
If you’re ready to get started with this major leap in Google Cloud language services, visit our NL API documentation, and to learn more about Google Cloud’s AI services, visit our AI and machine learning products page.
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Contact centers can transform customer experience using AI-driven speech analytics to evaluate every customer interaction and use it as a ‘data point’ to identify key patterns, enquiries, pain points, reviews and feedback to enhance customer experience with real-time, personalized recommendations. Knowlarity’s AI-based cloud telephony solutions for businesses built with Google Cloud takes speech analytics to another level!
Download the article to empower your contact centers with AI-powered speech analytics to make predictive analysis, reduce call handling time and volume as well as train contact center agents to provide customers with quick and real-time feedback.
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Document AI
Most business transactions begin, involve, or end with a document. But working with documents can be tricky, as leaders across industries seeking digital transformation can attest to.
These enterprises face similar challenges as they seek to extract information from documents. The process can be costly, time consuming, and prone to errors with manual data entry.
Learn how to use machine learning to organize, process, and extract data within documents. Also, learn about some examples of how various customers have found success using Google Cloud Document AI.
In this video Sudheera Vanguri, Product Manager, Google Cloud AI, highlights new Document AI capabilities. She walks you through of the building blocks of Document AI and demonstrates the new UI. She also highlights specialized Document AI models pre-trained for invoice and healthcare document processing as well as shows customer examples and live demos.
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