Amazon Simple Storage Service is simple web services interface that you can use to store and retrieve any amount of data, at any time, from anywhere on the web.
MonkeyLearn is a text analysis platform that helps you identify and extract actionable data from a variety of raw texts, including emails, chats, webpages, papers, tweets, and more! You can use custom tags to categorize texts, such as sentiments or topics, and extract specific data, such as organizations or keywords.Monkey Learn Integrations
Amazon S3 + Monkey LearnClassify Text in monkeylearn when New or Updated File is created in Amazon S3 Read More...
Amazon S3 + Monkey LearnExtract Text in monkeylearn when New or Updated File is created in Amazon S3 Read More...
Amazon S3 + Monkey LearnUpload training Data in monkeylearn when New or Updated File is created in Amazon S3 Read More...
Amazon S3 + GmailSend Email in Gmail when New or Updated File is created in Amazon S3 Read More...
It's easy to connect Amazon S3 + Monkey Learn without coding knowledge. Start creating your own business flow.
Triggers when you add or update a file in a specific bucket. (The bucket must contain less than 10,000 total files.)
Create a new Bucket
Creates a brand new text file from plain text content you specify.
Copy an already-existing file or attachment from the trigger service.
Classifies texts with a given classifier.
Extracts information from texts with a given extractor.
Uploads data to a classifier.
Amazon Simple Storage Service (Amazon S3. is a web service offered by Amazon Web Services. It is designed to make web-scale cloud storage more cost-effective. It provides a simple web services interface that can be used to store and retrieve any amount of data, at any time, from anywhere on the web. AWS offers it as part of its web services. (Wikipedia, 2017)
MonkeyLearn is an easy to use machine learning platform that lets you create intelligent apps and websites. It offers machine learning algorithms that help you analyze text, images, videos and audio files. You can also use their APIs and integrations to train your own models and deploy them. (Wikipedia, 2017)
. Integration of Amazon S3 and Monkey Learn
Amazon S3 has a powerful machine learning top which we can use for text analyzes and classifications. We can easily import our texts into the AWS platform, create classes and train them for different types. For example, we can train our model to classify new documents into one of the three categories. spam, not spam or unknown. The classification model will look at the features of the document, such as the number of words, and decide on its category. Once we have our model trained we can export the model using the MonkeyLearn API and re-import it to our AWS account so we can use it for new documents.
We will use the MonkeyLearn API to train our model and export it to AWS S3. For the data set we will use a corpus of emails which will be put into three different categories depending on what type of email they are
In order to do this we will first need to go to the MonkeyLearn website and create a new project. We will then need to login to our AWS account and create a new bucket. Once we have done this we will import our corpus using a program called Cyberduck. We will save all of our documents in a fpder called email_corpus . After importing our file we will need to create a new S3 bucket with a name of our choice e.g. emails . Once we have created our bucket we will upload all of our emails into the bucket by going to the bucket that we created and uploading all of our email files into it.
Now that we have SMS uploaded into our bucket we will need to download the files from our bucket by going to the bucket from your AWS conspe and clicking on “Download”. We will save all of our files as a .csv file on our computer so that we can upload it onto our MonkeyLearn project page. On the MonkeyLearn page we will click on “Import” and upload all of the files from the zipped fpder to our project page. Now that everything is uploaded we can train our model by clicking on “Train” on the left hand side of the screen. The next step is to pick what type of email you want to train your model for e.g. Spam or Not Spam etc.. We will select all emails through the drop down menu and click on “Continue Training”. Now that everything is set up we can start training our model by clicking on “Train” again under “Classifier 1” (see picture below. Now that everything has been uploaded into our MonkeyLearn account we can move onto the next step which is exporting our model so that it can be imported to AWS S3 for future use.
To export your model you need to click on “Export” under the Classifier 1 section. A popup box will appear asking you what format you would like your model in e.g. JSON or XML etc.. For this tutorial we will choose JSON as it is easier to read than other formats. By clicking “Export” you should now see your model under “Model 1” (see picture 2. You can now download your model or export it as you see fit. Now that our model has been exported we are now ready to import it into AWS S3 where it can be used later on for future classifications/labeling. To import our model via AWS S3 simply go back to your AWS conspe again and go to S3 where you should find your newly created bucket e.g. emails . Click on “Upload” at the top right corner where you should see a link that says “Add objects” (see picture 3. Clicking this link should open up a new window where you can upload your model by clicking on “Choose file” (see picture 4. Choose the JSON file that you exported from your MonkeyLearn project page earlier and upload your model by clicking on “Upload” at the bottom right corner (see picture 5.
The integration of Amazon S3 and Monkey Learn has many benefits, especially for data scientists who are interested in integrating machine learning into their workflows. It allows us to use existing models that were trained by experts without having to spend time training new models ourselves or even writing code for new models if they already exist online! MonkeyLearn has many different models to be used for different types of tasks e.g. sentiment analysis, text categorization etc.. Some of these models have been trained by top university professors so you know you are getting some high quality models! I hope this tutorial has been helpful if anyone would like some more information on how to get started with Amazon S3 and Monkey Learn I would recommend watching Adel Abouchaeed's video on YouTube here which explains how he uses MonkeyLearn for text classification with Amazon S3!
The process to integrate Amazon S3 and Monkey Learn may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.