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Sentiment Analysis

This plugin provides a recipe to estimate the sentiment polarity of text data

Plugin Information

Version 1.5.0
Author Dataiku (Alex COMBESSIE, Hicham EL BOUKKOURI)
Released 2018-07
Last updated 2021-04
License BSD 3-Clause License
Source code Github
Reporting issues Github

With this plugin, you will be able to estimate the sentiment polarity (positive/negative) of text data in English.

Table of contents

How to set up

Right after installing the plugin, you need to build its code environment.

Code Environment Creation
Code Environment Creation

This plugin requires Python version 2.7, 3.6, or 3.7 to be installed on the machine hosting DSS.

How to use

Let’s assume that you have a Dataiku DSS project with a dataset containing text data in English. This text data must be stored in a dataset, inside a text column, with one row for each document.

Navigate to the Flow, click on the + RECIPE button and access the Natural Language Processing menu. If your dataset is selected, you can directly find the plugin on the right panel.

Plugin Recipe Creation
Plugin Recipe Creation

Sentiment analysis recipe

Estimate the sentiment polarity (positive/negative) of text data in English


  • Text dataset: Dataset with a text column (in English)
Example of Text Dataset
Example of Text Dataset


Sentiment Analysis Recipe Settings
Sentiment Analysis Recipe Settings
  • Fill Input parameters
    • The Text column parameter lets you choose the column of your input dataset containing text data.
  • Choose Model parameters
    • Choose your Sentiment scale.
      • Either binary (0 = negative, 1 = positive) or 1 to 5 (1 = highly negative, 5 = highly positive).
      • Default is binary.
    • Choose whether to Output predictions as numbers and/or Output predictions as categories.
      • These parameters depend on the chosen Sentiment scale.
      • Default is yes to both.
    • Choose whether to Output confidence scores for the predicted sentiment polarity.
      • Confidence scores are from 0 to 1.
      • Default is false.


  • Output dataset: Copy of the input dataset with additional columns on predicted sentiment polarity
Example of Output Dataset
Example of Output Dataset

Happy natural language processing!

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