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Defining the training set and training the sentiment analysis model

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In the Sample construction step, split the data into the set that is used to train the model and the set that is used to test the model's accuracy.

  1. If you want to keep the split between the training and testing data as defined in the file that you uploaded, in the Construct training and test sets using field, select User-defined sampling based on "Type" column.

  2. If you want to ignore the split that is defined in the file and customize that split according to your business needs, perform the following actions:

    1. Select Uniform sampling.

    2. In the Training set field, specify the percentage of records that is randomly assigned to the training sample.

  3. Click Next.

  4. In the Model creation step, make sure that the Maximum Entropy check box is selected.

  5. Click Next.

    The model training and testing process starts.

  • Analyzing natural language

    Effortlessly analyze and extract meaningful information from large volumes of text with the use of text analytics. Based on your findings, you can further improve business performance and customer experience.

  • Determining the emotional tone of text

    Sentiment analysis determines whether the opinion that the writer expressed in a piece of text is positive, neutral, or negative. Knowledge about customers' sentiments can be very important because customers often share their opinions, reactions, and attitudes toward products and services in social media or communicate directly through chat channels.

  • Text analytics accuracy measures

    Models predict an outcome, which might or might not match the actual outcome. The following measures are used to examine the performance of text analytics models. When you create a sentiment or classification model, you can analyze the results by using the performance measures that are described below.

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