Sentiment Analysis Plugin

This plugin analyzes the sentiment of text within specified fields of records in a Flatfile Sheet. It operates during the data processing phase by hooking into the record commit lifecycle. For each designated text field, the plugin calculates a sentiment score, categorizes it as ‘positive’, ‘negative’, or ‘neutral’, and adds this information back to the record in two new fields: <field_name>_sentiment_score and <field_name>_sentiment_category. It also adds informational messages to the record detailing the outcome of the analysis. This is useful for automatically classifying customer feedback, product reviews, support tickets, or any other free-text data to quickly gauge user sentiment.

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Installation

Install the plugin using npm:

npm install @flatfile/plugin-enrich-sentiment

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Configuration & Parameters

The plugin accepts a configuration object with the following parameters:

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sheetSlug (required)

• Type: string
• Description: The slug of the sheet that this plugin should run on.

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textFields

• Type: string[]
• Description: An array of field keys (column names) that contain the text to be analyzed.
• Default: ['description']

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automaticValidation

• Type: boolean
• Description: A flag to enable or disable the sentiment analysis. If set to false, the plugin will add an info message to each record indicating that analysis is disabled but will not perform any analysis.
• Default: false - The analysis only runs if this is explicitly set to true.

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errorMessages

• Type: object
• Description: A configuration object for custom error messages. Note: This option is defined in the type interface but is not used in the current plugin implementation.

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Usage Examples

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Basic Usage

import { FlatfileListener } from '@flatfile/listener';
import { enrichSentiment } from '@flatfile/plugin-enrich-sentiment';

export default function(listener) {
  // Analyzes the 'description' field in the 'contacts' sheet.
  listener.use(enrichSentiment({
    sheetSlug: 'contacts',
    textFields: ['description'],
    automaticValidation: true
  }));
}

import { FlatfileListener } from '@flatfile/listener';
import { enrichSentiment } from '@flatfile/plugin-enrich-sentiment';

export default function(listener) {
  // Analyzes the 'description' field in the 'contacts' sheet.
  listener.use(enrichSentiment({
    sheetSlug: 'contacts',
    textFields: ['description'],
    automaticValidation: true
  }));
}

import { FlatfileListener } from '@flatfile/listener';
import { enrichSentiment } from '@flatfile/plugin-enrich-sentiment';

export default function(listener: FlatfileListener) {
  // Analyzes the 'description' field in the 'contacts' sheet.
  listener.use(enrichSentiment({
    sheetSlug: 'contacts',
    textFields: ['description'],
    automaticValidation: true
  }));
}

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Multiple Text Fields

import { FlatfileListener } from '@flatfile/listener';
import { enrichSentiment } from '@flatfile/plugin-enrich-sentiment';

export default function(listener) {
  // A more detailed configuration for a customer feedback sheet.
  // Analyzes both 'comment' and 'feedback' fields.
  listener.use(enrichSentiment({
    sheetSlug: 'customer-feedback',
    textFields: ['comment', 'feedback'],
    automaticValidation: true
  }));
}

import { FlatfileListener } from '@flatfile/listener';
import { enrichSentiment } from '@flatfile/plugin-enrich-sentiment';

export default function(listener) {
  // A more detailed configuration for a customer feedback sheet.
  // Analyzes both 'comment' and 'feedback' fields.
  listener.use(enrichSentiment({
    sheetSlug: 'customer-feedback',
    textFields: ['comment', 'feedback'],
    automaticValidation: true
  }));
}

import { FlatfileListener } from '@flatfile/listener';
import { enrichSentiment } from '@flatfile/plugin-enrich-sentiment';

export default function(listener: FlatfileListener) {
  // A more detailed configuration for a customer feedback sheet.
  // Analyzes both 'comment' and 'feedback' fields.
  listener.use(enrichSentiment({
    sheetSlug: 'customer-feedback',
    textFields: ['comment', 'feedback'],
    automaticValidation: true
  }));
}

​

Advanced Custom Usage

// This example shows how to use the exported helper functions
// inside a custom recordHook for more fine-grained control.
import { FlatfileListener } from '@flatfile/listener';
import { recordHook } from '@flatfile/plugin-record-hook';
import { performEnrichSentiment } from '@flatfile/plugin-enrich-sentiment';

export default function(listener) {
  listener.use(recordHook('reviews', async (record) => {
    const reviewText = String(record.get('review_text'));

    // Use the exported helper function directly
    const { error, result } = performEnrichSentiment(reviewText, 'review_text');

    if (error) {
      record.addWarning('review_text', error);
    } else if (result) {
      // Custom logic: only add a 'positive_review' flag
      if (result.category === 'positive' && result.score > 3) {
        record.set('positive_review', true);
        record.addInfo('review_text', 'This is a highly positive review!');
      }
    }
    return record;
  }));
}

// This example shows how to use the exported helper functions
// inside a custom recordHook for more fine-grained control.
import { FlatfileListener } from '@flatfile/listener';
import { recordHook } from '@flatfile/plugin-record-hook';
import { performEnrichSentiment } from '@flatfile/plugin-enrich-sentiment';

export default function(listener) {
  listener.use(recordHook('reviews', async (record) => {
    const reviewText = String(record.get('review_text'));

    // Use the exported helper function directly
    const { error, result } = performEnrichSentiment(reviewText, 'review_text');

    if (error) {
      record.addWarning('review_text', error);
    } else if (result) {
      // Custom logic: only add a 'positive_review' flag
      if (result.category === 'positive' && result.score > 3) {
        record.set('positive_review', true);
        record.addInfo('review_text', 'This is a highly positive review!');
      }
    }
    return record;
  }));
}

// This example shows how to use the exported helper functions
// inside a custom recordHook for more fine-grained control.
import { FlatfileListener } from '@flatfile/listener';
import { recordHook } from '@flatfile/plugin-record-hook';
import { performEnrichSentiment } from '@flatfile/plugin-enrich-sentiment';

export default function(listener: FlatfileListener) {
  listener.use(recordHook('reviews', async (record) => {
    const reviewText = String(record.get('review_text'));

    // Use the exported helper function directly
    const { error, result } = performEnrichSentiment(reviewText, 'review_text');

    if (error) {
      record.addWarning('review_text', error);
    } else if (result) {
      // Custom logic: only add a 'positive_review' flag
      if (result.category === 'positive' && result.score > 3) {
        record.set('positive_review', true);
        record.addInfo('review_text', 'This is a highly positive review!');
      }
    }
    return record;
  }));
}

​

Using Utility Functions

import { analyzeSentiment } from '@flatfile/plugin-enrich-sentiment';

const analysis = analyzeSentiment("Flatfile is an amazing tool!");
// analysis -> { score: 6, category: 'positive' }

import { analyzeSentiment } from '@flatfile/plugin-enrich-sentiment';

const analysis = analyzeSentiment("Flatfile is an amazing tool!");
// analysis -> { score: 6, category: 'positive' }

import { analyzeSentiment } from '@flatfile/plugin-enrich-sentiment';

const analysis = analyzeSentiment("Flatfile is an amazing tool!");
// analysis -> { score: 6, category: 'positive' }

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API Reference

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enrichSentiment(config: EnrichSentimentConfig)

The main entry point for the plugin. It creates and registers a recordHook with Flatfile that performs sentiment analysis on incoming records based on the provided configuration.

Parameters:

• config (EnrichSentimentConfig): An object containing configuration options
  • sheetSlug (string): The slug of the sheet to target
  • textFields (string[]): An array of field keys to analyze. Defaults to ['description']
  • automaticValidation (boolean): Must be true to enable analysis

Returns: A function of type (listener: FlatfileListener) => void which is used to install the plugin into a Flatfile listener.

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analyzeSentiment(text: string)

A pure function that takes a string of text and returns its sentiment analysis.

Parameters:

• text (string): The input text to analyze

Returns: An object with the following properties:

• score (number): A numerical score representing the sentiment. Positive values are positive, negative values are negative
• category (‘positive’ | ‘negative’ | ‘neutral’): A string category for the sentiment

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performEnrichSentiment(value: string, field: string)

A wrapper around analyzeSentiment that includes basic validation and formats the output for use within a record hook. It checks for empty input and structures the return value with either an error or a result object.

Parameters:

• value (string): The text value from the record field
• field (string): The name of the field being analyzed, used for creating informative messages

Returns: An object with one of two shapes:

• On success: { error: null, result: { score: number, category: string, message: string } }
• On error (empty input): { error: string, result: null }

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Troubleshooting

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Empty Text Fields

If a text field specified in the textFields configuration is empty or null, the plugin will not throw an error. Instead, it will add a warning to the record using record.addWarning() with a message like “No text found for sentiment analysis in field: [field_name]”.

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Analysis Not Running

If automaticValidation is set to false or is omitted, the plugin will not perform any analysis or add any warnings. It will only add an informational message to the record stating that automatic analysis is disabled.

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Notes

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Default Behavior

• The plugin is disabled by default. You must explicitly set automaticValidation: true to enable sentiment analysis.
• If no textFields are specified, the plugin will default to analyzing the description field.

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Generated Fields

The plugin adds two new fields to each processed record for each analyzed text field:

• <field_name>_sentiment_score: Contains the numerical sentiment score
• <field_name>_sentiment_category: Contains the sentiment category (‘positive’, ‘negative’, or ‘neutral’)

Ensure your Sheet configuration or downstream systems can handle these new fields.

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Configuration Considerations

• The configuration property to enable the plugin is automaticValidation (boolean), not autoAnalysis.
• The errorMessages property in the EnrichSentimentConfig type is defined but not currently implemented in the plugin’s logic.
• The plugin relies on the sentiment npm package for its analysis logic.