One of the most vibrant elements of culture is the use of native languages and the time-honored tradition of storytelling. Anthropologists and linguists have been vocal on the role that language plays in the preservation of culture and how it contributes to the appreciation of heritage. 

Unfortunately, of the more than 7,000 languages that are spoken around the globe, nearly 3,000  are at risk of disappearing. In fact, it’s estimated that on average a language becomes extinct every fourteen days. Google Arts & Culture realized that with some creative technology and partnering with language organisations, we could help create an interactive and educational tool to help promote them.

Enter Woolaroo, an open-source photo-translation platform powered by machine learning and image recognition. The application was built on Google Cloud to encourage users to explore endangered languages around the world. Users are able to take a picture of an object in real-time, and the application returns the word in its native language, along with its pronunciation. 

Woolaroo was created with the philosophy that learning languages is greatly enhanced through engagement and context. By seeing an object in its environment, it’s easier to retain the information and then use it more naturally in conversation. 

With the help of Googlers, Woolaroo was launched in 10 languages, including Calabrian Greek, Louisiana Creole, Maori and Yiddish. During the conception stage of the app, teams from Partner Innovation and Google Arts & Culture put out an open call to the rest of Google to see what lesser-known languages our employees spoke. They then worked with the individuals that responded to develop dictionaries that were reviewed by partner institutions to ensure translations were correct and consistent. 

Woolaroo uses Google Cloud Vision API, which derives insights from images using AutoML or pre-trained models to quickly classify images into millions of predefined categories. This makes AI accessible and useful to more people as AutoML automates the training of these machine learning models.

Our team at Google Arts & Culture creates immersive experiences for people to learn about art, history, culture and more. We are committed to supporting the preservation of heritage and cultural landmarks – including spoken language – through the use of modern technology. The magic of Woolaroo is that it is open source, which means any person or organisation can use it to build something for their own endangered language. To learn about the efforts Google Arts & Culture is involved in, download the Google Arts & Culture app or visit our blog.

In the competitive world of consumer goods, sustainability matters more than ever. In recent Google survey, 82% of consumers said sustainability is more top of mind now than it was before COVID-19¹ and 78% said that big businesses have a role to play in helping to fight climate change.² As well as delivering on customers’ heightened expectations, sustainable business practices can help organizations reduce waste and lower operational costs and even help attract top talent.

Not only is sustainability good for business performance and the bottom line, it’s great for the planet, too. And it’s something we care deeply about at Google. Since our earliest days, we have focused on developing services that significantly improve the lives of billions of people while operating our business in an environmentally sustainable way. In 2007, we were the first major company to become carbon neutral, and in 2017 we were the first to achieve 100% renewable energy. Today, we proudly operate the cleanest cloud in the industry. We’ve also been powering humanitarian, scientific, and environmental initiatives studying geospatial information and making it available for analysis via Google Earth Engine. 

Powering the future of responsible sourcing 

How does all this relate to CPG supply chains? The world relies on raw materials like palm oil, soy, and cocoa to produce the items we consume every day—such as coffee, chocolate, frozen foods, shampoo, toothpaste, cosmetics, and even household cleaning products. Yet as demand for these materials continues to grow, forests are under threat. Change is needed.

As sustainability moves further into the spotlight, in-demand crops face increased supplier scrutiny. Environmentally conscious companies want to know what percentage of their raw materials is sourced from deforestation-free suppliers—and how they can improve that number. Until recently, many CPG brands have found it hard to get real-time, reliable visibility into operations at a local supplier level, globally. 

Google Cloud, in partnership with NGIS, is helping brands gain a deeper understanding of raw material sourcing practices across supplier networks, so they can improve supplier performance and compliance in the fight against deforestation. The TraceMark solution, developed by NGIS, uses Google Earth Engine and BigQuery to analyze and visualize how suppliers behave over time. 

Google Earth Engine is the world’s largest archive of open Earth data. It combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities to help people detect changes, map trends, and quantify differences on the Earth’s surface.With Earth Engine, organizations can analyze the potential social, and environmental impacts of the decision they make. 

Together, TraceMark, Google Earth Engine and BigQuery help companies visualize, monitor, and measure the impact of suppliers’ farming practices. The process starts with Earth Engine, which aggregates and harmonizes planetary data into images that it has been collecting for decades. Then, boundary maps for different suppliers are created and NGIS applies climate data science and machine learning to turn pixels into insights. Finally, BigQuery and Vertex AI -Google Cloud’s unified artificial intelligence platform are used to produce supplier scoring, which is integrated into downstream ERP and procurement systems to support more informed decision-making.

These efforts support the responsible sourcing of raw materials by making supply chains more agile, transparent and traceable.

“NGIS is thrilled to work with Google Cloud and its partners to enable business accountability for sustainable practices at all levels of their supply chain,” said Nathan Eaton, Executive Director, NGIS. “Google Earth Engine provides unique geospatial capability that [gives] leaders visibility and control over their environmental footprint and that of their suppliers in a way that was not previously possible.” 

Unilever commits to working towards a deforestation-free supply chain 

Unilever is a great example of a company committed to using technology in the quest to become more sustainable. Since 2020, Unilever has partnered with Google Cloud to use data for eco-friendly decision-making, particularly when it comes to sustainable commodity sourcing. 

By combining the power of cloud computing with satellite imagery and AI, we’re helping Unilever build a more holistic view of the forests, water cycles, and biodiversity that intersect its supply chain. By gaining a complete picture of these ecosystems, Unilever can detect deforestation while simultaneously prioritizing critical areas of forest and habitats in need of protection.

The cleanest cloud for your sustainable transformation

We are excited to bring Google Earth Engine and NGIS to our CPG customers to help them meet their sustainability goals. Looking ahead, we’re committed to furthering our own ambitious sustainability goals and empowering CPG brands with the technology to do more for our environment and our shared future. 

Contact your cloud seller to learn more about Tracemark sustainable sourcing solutions and how Google Cloud can help you advance your sustainability initiatives.

Explore TraceMark on the Google Cloud Marketplace

To know more about how we are helping CPGs transform digitally read this ebook

<sup>1. Google/C Space, BR, FR, DE, IN, MX, U.K., U.S., qualitative survey activity, n = 528, Nov. 24–Nov. 26, 2020.
2. Google/Ipsos, Google Sustainability, BR, FR, DE, IN, JP, U.K., U.S., n=16,959 online population 18–70, July 2021.</sup>

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:

1. Build a vocabulary list using the review column
2. Convert the review column into sparse tensors
3. Train a classification model using the sparse tensors to predict the label (“positive” or “negative”)
4. 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.