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Uploading data for training and testing of the sentiment analysis model

Version:

In the Source selection step, select the source for training and testing data that is required to create a model.

  1. Optional:

    To view the required structure of the training and testing data as well as sample records, click Download template.

  2. Click Choose file.

  3. Select a .csv, .xls, or .xlsx file with sample records for training and testing the model in your directory.

    The file must contain sample records with the assigned sentiment values.

    Ensure that the sentiment categories in the file that you upload match the sentiment categories that you specified in the Lexicon selection step.
  4. Click Next.

  • 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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