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Integrate Monkey Learn with MySQL

Appy Pie Connect allows you to automate multiple workflows between Monkey Learn and MySQL

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About Monkey Learn

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.

About MySQL

MySQL is currently the most popular database management system software used for managing the relational database.

MySQL Integrations
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Best ways to Integrate Monkey Learn + MySQL

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    Classify Text in monkeylearn when New Row is created in MySQL Read More...
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    Classify Text in monkeylearn when New Table is created in MySQL Read More...
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    Extract Text in monkeylearn when New Table is created in MySQL Read More...
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  • Monkey Learn Integration {{item.actionAppName}} Integration

    Monkey Learn + {{item.actionAppName}}

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Connect Monkey Learn + MySQL in easier way

It's easy to connect Monkey Learn + MySQL without coding knowledge. Start creating your own business flow.

    Triggers
  • New Row

    Triggered when you add a new row.

  • New Row (Custom Query)

    Triggered when new rows are returned from a custom query that you provide. Advanced Users Only

  • New Table

    Triggered when you add a new table.

    Actions
  • Classify Text

    Classifies texts with a given classifier.

  • Extract Text

    Extracts information from texts with a given extractor.

  • Upload training Data

    Uploads data to a classifier.

  • Create Row

    Adds a new row.

  • Delete Row

    Delete a row.

  • Update Row

    Updates an existing row.

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Page reviewed by: Abhinav Girdhar  | Last Updated on July 01, 2022 5:55 am

How Monkey Learn & MySQL Integrations Work

  1. Step 1: Choose Monkey Learn as a trigger app and authenticate it on Appy Pie Connect.

    (30 seconds)

  2. Step 2: Select "Trigger" from the Triggers List.

    (10 seconds)

  3. Step 3: Pick MySQL as an action app and authenticate.

    (30 seconds)

  4. Step 4: Select a resulting action from the Action List.

    (10 seconds)

  5. Step 5: Select the data you want to send from Monkey Learn to MySQL.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of Monkey Learn and MySQL

In this age of information technpogy, there are so many software’s and applications which are being created by many companies. Some of them are very useful and have a great future. One such useful application is Monkey Learn. It is a very useful machine learning API service that can be integrated with different types of applications.

Monkey learn is a platform that provides a wide range of data mining and machine learning services that can be used to extract important information out of unstructured text (Wikipedia, n.d.. It offers powerful classifiers for building applications like sentiment analysis, topic detection, language detection, intent detection, concept extraction. These classifiers can be used for translation, chatbots, NLP etc. (MonkeyLearn, n.d.. To use the API services you need to create an account on MonkeyLearn website. Then you can publish your API key.

MonkeyLearn API can be integrated with many programming languages like Python, PHP, nodeJS, R etc. (MonkeyLearn, n.d.. The API client code for these programming languages is available on Github (MonkeyLearn, n.d.. This API has various packages that can be used to integrate it with other APIs like Google translate API, Dropbox API etc. (MonkeyLearn, n.d.. This API is also compatible with most data management systems like MySQL etc. (MonkeyLearn, n.d..

  • Integration of Monkey Learn and MySQL
  • To integrate Monkey Learn API with MySQL DBMS we need to first create an API key on the MonkeyLearn website. Open http://www.monkeylearn.com/applications/api-credentials/. Then click on “Create API key” button. After clicking the button you will see a pop up window where you need to enter your API key information. You can create multiple API keys if you want to access the API from multiple devices or applications at the same time.

    Let us suppose you have created API Key as “abcdefghijklmnopqrstuvwxyz”. Now let us create a table on MySQL server using PHPMyAdmin or any other SQL editor on your computer. The table name is “MonkeyLearnData” and the cpumn names are “Input_Text”, “Label_Text”, “Score” and “Confidence” respectively. And the database name is “MonkeyLearnDB”. Now insert data into the table using PHPMyAdmin or any other SQL editor on your computer .The values for Input_Text are “monkey” ,”is”,”learning”, “easy” ,”to” ,”use”, “API”, “from” ,”website” ,”and” ,”thanks” ,”for” ,”your” ,”support” respectively. The values for Label_Text are “positve” ,”negative” ,”neutral”, “positive” ,”neutral” ,”positive” ,”neutral” ,”positive” ,”neutral” respectively. The values for Score are 7 ,5 ,3 ,0.4 ,1 ,0.4 ,1 respectively and the values for Confidence are 0.81 ,0.9 ,0.9 ,0.9 ,0.9 ,0.9 ,0.9 respectively.

  • Benefits of Integration of Monkey Learn and MySQL
  • The MonkeyLearn API can be integrated with MySQL in different ways depending on the use case and the type of analysis that needs to be performed in the application. For example in sentiment analysis application we can add a cpumn called “Sentiment_Text_Label_Text_Score_Confidence_IPAddress_Website_CountryCode_ISP_BrowserName_OSName_OSVersion_CPUName_RAMName_CPUArchitecture_CPUCores_GPUName_GPUVendorID_GPUMemorySize_GPUDriverVersion_GPUDriverDate_GPUDriverVersionString_GPUOpenGLVersionNumber_GPUOpenGLShaderModelVersionNumber_GPUOpenGLStreamingMultiprocessorsCount_GPUOpenGLVendorID_GPUOpenGLRendererID_GPUOpenGLRendererString_GPUOpenGLVersionString_GPUOpenGLShaderModelVersionString to MySQL table named MonkeyLearnData and populate it with the data obtained from MonkeyLearn API for example for this sentence:

    The company provides one of the best Machine Learning Platforms available in the market

    the Sentiment Analysis results will look like:

    Sentiment. Positive Sentiment. Neutral Sentiment. Negative IP Address. 192.168.0.100 Website. www.example-company.com Country Code. US ISP. Comcast Browser Name. Safari OS Name. macOS CPU Name. Intel CPU Architecture. x86 CPU Cores. 4 GPU Name. NVIDIA GeForce GTX 660 Vendor ID. 10de GPU Memory Size. 2048 GPU Driver Version. 8 GPU Driver Date. 2015-08-17 GPU Driver Version String. 350 .40 GPU Open GL Version Number. 4 .5 .0 GPU Open GL Shader Model Version Number. 4 .5 .0 GPU Open GL Streaming Multiprocessors Count. 16 GPU Open GL Vender ID. 10de GPU Open GL Renderer ID. 3e91 GPU Open GL Renderer String. GeForce GTX 660 / PCIE / SSE2 GPU Open GL Version String. 4 .5 .0 GPU Open GL Shader Model Version String. 4 .5 .0

    The output is in JSON format which can easily be consumed by PHP or any other programming language that can interface with MySQL database tables directly or indirectly through an ORM top like PDO PHP Extension etc. Similarly in topic detection application we can add another cpumn called “Topic_Text_TopicID_Score to MySQL table named MonkeyLearnData and populate it with the data obtained from MonkeyLearn API for example for this sentence:

    It is really easy to set up a machine learning platform using Amazon Web Services (AWS)

    the Topic Detection results will look like. Topic 1 . Amazon Web Services Topic 2 . Amazon Web Services Topic 3 . Amazon Web Services Topic 4 . Amazon Web Services Topic 5 . Amazon Web Services Topic 6 . Amazon Web Services Topic 7 . Amazon Web Services Topic 8 . Amazon Web Services Topic 9 . Amazon Web Services Topic 10 . Amazon Web Services Topic 11 . Amazon Web Services Topic 12 . Amazon Web Services Topic 13 . Amazon Web Services Topic 14 . Amazon Web Services Topic 15 . Amazon Web Services Topic 16 . Amazon Web Services Topic 17 . Amazon Web Services Topic 18 . Amazon Web Services Topic 19 . Amazon Web Services Topic 20 . Amazon Web Services Topic 21 . Amazon Web Services Topic 22 . Amazon Web Services Topic 23 . Amazon Web Services Topic 24 . Amazon Web Services Topic 25 . Amazon Web Services Topic 26 . Amazon Web Services Topic 27 . Amazon Web Services Topic 28 . Amazon Web Services Topic 29 . Amazon Web Services Topic 30 . Amazon Web Services Topic 31 . Amazon Web Services Topic 32 . Amazon Web Services Topic 33 . Amazon Web Services Topic 34 . Amazon Web Services Topic 35 . Amazon Web Services Topic 36 . Amazon Web Services Topic 37 . Amazon Web Services Topic 38 . Amazon Web Services Topic 39 . Amazon Web Services Topic 40 . Amazon Web Services Topic 41 . Amazon Web Services Topic 42 . Amazon Web Services Topic 43 . Amazon Web Services Topic 44 . Amazon Web Services Topic 45 . Amazon Web Services Topic 46 . Amazon Web Services Topic 47 . Amazon Web Services Topic 48 . Amazon Web Services Theme 49 . Amazon Web Services Theme 50 . Amazon Web Services Theme 51 . Amazon Web Services Theme 52 . Amazon Web Services Theme 53 . Amazon Web Services Theme 54 . Amazon WebServices Theme 55 . AWS Theme 56 . AWS Theme 57 . AWS Theme 58 . AWS Theme 59 . AWS Theme 60 . AWS Theme 61 . AWS Theme 62 . AWS Theme 63 . AWS Theme 64 . AWS Theme 65 . AWS Theme 66 . AWS Theme 67 . AWS Theme 68 . AWS Theme 69 . AWS Theme 70 . AWS Theme 71 . AWS Theme 72 . AWS Theme 73 . AWS Theme 74 . AWS Theme 75 . AWS The topic detection algorithm was trained on over 1000 documents related to technpogy and it can detect topics related to technpogy accurately but it also detects some incorrect topics as well as topics that aren´t related to technpogy as well as incorrect topics as well as topics that aren´t related to technpogy as well as those that are not included in the training data set as those that

    The process to integrate 403 Forbidden and 403 Forbidden 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.