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You want to create a project that enables you to label the sentiment in a given Tweet post as “Positive” Positive, “Negative” Negative, “Neutral” Neutral, or “Not Not Sure.”
To create this project, you must perform the following tasks:
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Identify the sentiment type in a given Tweet based on four categories “Positive”, “Negative”, “Neutral”, and “Not Sure.”as Positive, Negative, or Neutral.
In the cases where the sentiment cannot be determined by you, mark it as Not Sure.
Sample Input Data
To illustrate an example, we shall copy any Tweet text from Twitter.
To get started, select the tweet texts to be labeled and paste them You select sample tweets that you wish to annotate. You paste the text of the tweets into a Microsoft Excel sheet under the a column labeled Tweet.
Also, add a column labeled Tweet# to number multiple tweets.
2. Save You save the Microsoft Excel sheet as Tweet_Tagging_Input.xlsx on your hard drive.
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Download Source File
Alternatively, you can download and use this file for You can follow the steps listed above to create your Microsoft Excel Sheet or download this file, unzip it, and use it in your project: Tweet_tagging_input.xlsx.
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Configuring Project Metadata
Project Metadata is the first section you will be redirected to tab that appears when you create a project. Here, provide the The Project Metadata tab enables you to provide basic information, such as the project name, process, and project type, associated with your project. You can also upload any help documentationdocumentation that you may want to add to your project.
For detailed information on adding working with the Basic project information under Info section of the Project Metadata tab, see Add Basic Project Information.
For detailed information on adding the project documentationworking with the Basic Info section of the Project Metadata tab, see Manage Project Documentation.
To create the project, click the Create Project floating button to the left side of the Projects page.
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The Project Metadata tab associated with your new project appears.
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2. Enter Twitter Post Analysis as the Project Name
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and Sentiment Identification
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as the Process.
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3. Click Project Type > Text-Based
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As your project doesn't require lookup data, keep the Lookup checkbox unselected.
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Keep the Enable Project Pipeline option unselected which is the default setting.
4. Click Next.
The Documents tab is selected where all the Project Documents available are listedsub-tab appears.
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5. Click the UPLOAD DOCUMENT button to You can upload documents associated with the project if required. This is an optional step which will be skipped , and you can skip it for now.
6. Click Next. 7.
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The Task Design section appears where you can tab appears. Use this tab to manage your project input and output fields.
Managing Project Input and Output Fields
A project input field determines what is to be labeled (in this case, tweet post sentences and words) while an output field determines what values the labeling analysts should use for labeling (in this case, the sentiment types “Positive”, “Negative”, “Neutral”, or “Not Sure”). Your Project input and output fields are key elements that determine what happens in your project. The input fields that you specify here will appear as available options for input in your project. Similarly, the output fields that you configure here will appear as output options in your project execution UI. In other words, your project can only uptake and output data associated with the input and output fields that you create here.
Taskmonk uses the project type that you specify to add input and/or output fields to projects as required. You can modify these later. Here, In this instance, you selected Text-Based is selected as the Project Type. Taskmonk does not add any fields to the Task Design section.
Creating and Configuring the Input Fields
To create and configure the input fields, follow these steps:
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Either click the BROWSE INPUT FILE button to extract field names from the sample input file you’ve downloaded in the previous step, or
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Click the CREATE INPUT FIELD button to create the input fields explicitly.
, and select the field type for the input and output fields you’ve created under Task Design.
Click the Input Field tab to display the Input Field UI.
Click the Create Input Field button.
To edit the input field details:
Click the Edit Field Name icon and enter Tweet in the Name textbox.
Click the Update Field Name icon next to the Name textbox to save the changes.
Click the Field Type dropdown and set it to Text.
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4. In this example, we’ve created two input fields,
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Tweet#and
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Tweet. Set Field Type to text, Mandatory to True, and Operational to True for both these fields.
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Creating
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To create and configure the output field, follow these steps:
Output Field Details
Click the Output Field tab to display the Output Field UI.
2. Click the CREATE OUTPUT FIELD button. 
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23. In the Create Output Field modal window, set the values for the following:
Name: Provide the output field name.
(Optional) Visible Name: The name that appears in the output file.
Set Data Type: Select from the listed options how the output field options will appear. In this case, select DropDown Radio.
Possible Values: Type in comma-separated values that this output field can take. These values will be listed for radio button selection in the dropdown list.
34. Select the All Levels check box to indicate that this field must be available to users at all execution levels, such as labeling, quality analysis, and so on.
45. Click Create to save the new output field and return to the Task Design > Output Fields tab.
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56. Click Next to move to the next step, and click the Quality Workflow tab.
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Creating Quality Workflows
The Quality Workflow section is where you can tab enables you to specify how output quality checks will be carried out and from which execution level to which execution level. In this example, we create the following user levels where Level 1 represents the lowest Majority Vote level and Level 3 represents the highest.
Twitter Sentiment Labeler: The labeling analyst representing execution Level 1.
Twitter Sentiment QA: The quality analyst representing execution Level 2.
Twitter Sentiment Project Lead: The labeling project lead representing execution Level 3.
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you want to ensure output quality. It also helps you create the execution levels required for your quality workflows. For example, you want to create the following levels for this project:
Annotator
Quality Analyst
Delivery Lead
In this instance, you want to enable a Majority-Vote workflow. Click the Execution Method field and select Majority-Vote from the drop-down list that appears.
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2. By default, Taskmonk creates the Analyst role for you.
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Click the Update Execution Level icon.
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The Edit Execution Level modal
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4. Enter Twitter Sentiment Labeler1 appears.
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3. Enter Annotator as the Execution Level Name. By default, Level 1 is assigned to labelers where Skip Task can be Enabled or Disabled and Reject Task is Disabled automatically. You can choose to skip these field inputs if required
4. You can skip all other fields here. Click Update to return to the Quality Workflow tab.
5. Click the Add Execution Level button on the right side of the page. The Add Execution Level modal appears.
6. Enter Quality Analyst in the Execution Level Name field.
7. You can skip all other fields here. Click Add.
Taskmonk adds the new execution level, closes the modal, and updates displays the updated Quality Workflow section. Repeat the above steps to add Quality Analyst at Level 2 and Twitter Sentiment Project Lead at Level 3.
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7. Click Next to move to the next step Execution.
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8. Next, add the User counts, Majority vote, and Consensus Percentage values by follow ing these steps:
process to add all required execution levels.
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8. Click Next to update User counts and Majority vote.
a. Set the User counts to 3 if every task has to be allocated to 3 different users. Majority vote 2 indicates that out of 3 users, if at least 2 of them provide the same response, the task completes at level 1. If all 3 users provide different responses, the task moves to level 2.
b. Consensus Percentage: Percentage of total tasks imported that should be allocated to multiple users. The remaining tasks will follow the Maker-Checker or Maker-Editor model.
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Creating the Process Logic
The Process Logic section lets you configure the logic based on which datasets are moved from one execution level to the next. For the current project, we enable the following rules:
In case of Majority Vote, do not create any rule from level 1 to level 2. All the tasks which do not have a majority output will automatically flow to level 2.
40% of the project datasets move from level 2 to level 3.
To set the Process Logic, follow these steps:
In the Process Logic section, click the +ADD PROCESS LOGIC button to create the logical flow based on which datasets are moved from one execution level to the next.
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2. In the ADD PROCESS LOGIN section, select L1 [TWITTER SENTIMENT LABELER] from the dropdown list for From Level, and L2 [TWITTER SENTIMENT QA] for To Level.
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3. Click the Add Rule icon to load the add rule UI.
4. Click the USE THE ADD BUTTON TO START ADDING RULES icon.
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7. Click the Percentage drop-down and select 40 %.
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8. Select Project for Percentage Scope and click Submit.
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9. Repeat the above steps to add any other rules by selecting the required options for Percentage Scope and Combinator. Your final Process Logic screen will look like the following:
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Managing Users and Roles
You must now add users to your project and assign the execution levels you just created to them.
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4. Click Add to add the selected users to their respective execution levels.
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The users get added successfully to the Manage Users page.
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Click the Datasets tab. The Datasets page appears. Use this page to manage datasets for your project.
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2. Taskmonk organizes datasets into batches to simplify management and tracking. To add a new dataset, click Add Batch on the right side of the page. The Add Batch modal appears.
3. Enter Batch 1 as the name for the batch that you want to import in the Add New Batch field. You can ignore the other fields.
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4. Click Submit. This creates a new batch of data for your project and adds it to the Pending tab of the Datasets page.
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5. You can now upload datasets into the batch, as required. To add a dataset to the batch, click the Import button under the Tasks(Import/Export) column. The Import Task modal appears.
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6. Click Choose Files, select the sample input file from your computer and click Import.
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Labeling Text Using Taskmonk
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Log in as Analyst and click the My Tasks icon at the top of the page . The to view the My Tasks page appears.
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2. Click the Get Tasks button adjacent to the Twitter Post Analysis project. The labeling UI associated with this project appears.
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3. You can see the following project details in the labeling UI:
Batch Name (Batch 1) in at the top-left section of the page.
Input fields (Text) on at the top half of the page.
Output/Result fields on at the bottom half of the page.
4. Select the option for Sentiment Type and click Submit & Get Next Task.
5. Repeat the step until all tasks are completed
For detailed information on working with a typical labeling UI, see Labeling Data.
Viewing Batch Status Report
Sign in as Manager, go Go to the Projects page and click Reports > View for the Twitter Post Analysis project. The Reports page appears.
Click Dataset Progress Reports to view the batch status report. This shows the total number of tasks pending and completed at each level for all batches. The Disagreement score indicates the total number of tasks without a majority at level 1. In other words, the total tasks which moved to level 2.
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This completes our example for Twitter Sentiment Identification using Majority Vote. Thank you!
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Downloadable Sample Files
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