Deepopinion Workflow | Scribe

    Deepopinion Workflow

    • Ahmed Al-Ali |
    • 0 step |
    • 69 minutes
    Log-in to DeepOpinion Studio [](
    Give your model a name, here we named it "Hotels Reviews" On model type choose "Aspect-based Sentiment Analysis" from the drop down menu for creating opinion mining model Click "CREATE"
    You will then find your new custom model added to your page. Click on it to start building your model

    Custom Model Building

    Tip! Creating a custom model happens in 4 simple steps: 1. Data Upload 2. Aspect Definition (listing the topics you want the model to detect) 3. Training Data (annotating examples that the AI model can learn from) 4. Model Training (the model here builds the required skills to solve your task based on example provided in the training data)
    Tip! If you have training data you can directly jump to step 3

    1. Upload Text Data

    You will now upload your text data. Your file format can be "xlsx", "csv", "json", or "jsonl" Click "Upload Documents" Click "BROWSE FILES" and select your file Select the column that has the text by ticking the checkbox Click "CREATE DOCUMENT GROUP"
    To build an intuition for the data you can search for keywords and check if they appear in the text. This can help you in deciding on the topics if you don't have a predefined list
    Use the back arrow to return to the document upload page to upload more files as needed

    2. Define Topics

    Now Navigate to Step 2 where you will list the topics that you want the model to detect in the text.
    Tip! You can either add topics manually or upload the whole list at once
    Click "Add an aspect"
    Type "Hotel [[enter]]"
    Click this icon.
    Type "Food [[enter]]"
    You can choose to upload your topics list as a "csv" file Click "UPLOAD ASPECTS" Click "BROWSE FILES" Click "UPLOAD ASPECTS"
    You can also review, edit, or delete topics at any time Avoid doing that after annotating data. It is tidier to create a new model if you would like to edit topics after training

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