Creating a text analysis experiment
Creating a text analysis experiment Use AutoAI's text analysis feature to perform text analysis of your experiments. For example, perform basic sentiment analysis to predict an outcome based on text comments. Note: Text analysis is only available for AutoAI classification and regression experiments. This feature is not available for time series experiments. Text analysis overview When you create an experiment that uses the text analysis feature, the AutoAI process uses the word2vec algorithm to transform the text into vectors, then compares the vectors to establish the impact on the prediction column. The word2vec algorithm takes a corpus of text as input and outputs a set of vectors. By turning text into a numerical representation, it can detect and compare similar words. When trained with enough data, word2vec can make accurate predictions about a word's meaning or relationship to other words. The predictions can be used to analyze text and guess at the meaning in sentiment analysis applications. During the feature engineering phase of the experiment training, 20 features are generated for the text column, by using the word2vec algorithm. Auto-detection of text features is based on analyzing the number of unique values in a column and the number of tokens in a record (minimum number = 3). If the number of unique values is less than number of all values divided by 5, the column is not treated as text. When the experiment completes, you can review the feature engineering results from the pipeline details page. You can also save a pipeline as a notebook, where you can review the transformations and see a visualization of the transformations. Note: When you review the experiment, if you determine that a text column was not detected and processed by the auto-detection, you can specify the text column manually in the experiment settings. In this example, the comments for a fictional car rental company are used to train a model that predicts a satisfaction rating when a new comment is entered. Watch this short video to see this example and then read further details about the text feature below the video. Video disclaimer: Some minor steps and graphical elements in this video might differ from your platform. This video provides a visual method to learn the concepts and tasks in this documentation. * Transcript Synchronize transcript with video Time Transcript 00:00 In this video you'll see how to create an AutoAI experiment to perform sentiment analysis on a text file. 00:09 You can use the text feature engineering to perform text analysis in your experiments. 00:15 For example, perform basic sentiment analysis to predict an outcome based on text comments. 00:22 Start in a project and add an asset to that project, a new AutoAI experiment. 00:29 Just provide a name, description, select a machine learning service, and then create the experiment. 00:38 When the AutoAI experiment builder displays, you can add the data set. 00:43 In this case, the data set is already stored in the project as a data asset. 00:48 Select the asset to add to the experiment. 00:53 Before continuing, preview the data. 00:56 This data set has two columns. 00:59 The first contains the customers' comments and the second contains either 0, for "Not satisfied", or 1, for "Satisfied". 01:08 This isn't a time series forecast, so select "No" for that option. 01:13 Then select the column to predict, which is "Satisfaction" in this example. 01:19 AutoAI determines that the satisfaction column contains two possible values, making it suitable for a binary classification model. 01:28 And the positive class is 1, for "Satisfied". 01:32 Open the experiment settings if you'd like to customize the experiment. 01:36 On the data source panel, you'll see some options for the text feature engineering. 01:41 You can automatically select the text columns, or you can exercise more control by manually specifying the columns for text feature engineering. 01:52 You can also select how many vectors to create for each column during text feature engineering. 01:58 A lower number faster and a higher number is more accurate, but slower. 02:03 Now, run the experiment to view the transformations and progress. 02:09 When you create an experiment that uses the text analysis feature, the AutoAI process uses the word2vec algorithm to transform the text into vectors, then compares the vectors to establish the impact on the prediction column. 02:23 During the feature engineering phase of the experiment training, twenty features are generated for the text column using the word2vec algorithm. 02:33 When the experiment completes, you can review the feature engineering results from the pipeline details page. 02:40 On the Features summary panel, you can review the text transformations. 02:45 You can see that AutoAI created several text features by applying the algorithm function to the column elements, along with the feature importance showing