What is Text Analytics? Examples with plainSignal and GA4

Text analytics

Text Analytics applies algorithms and NLP to extract insights from unstructured text data for analysis, sentiment, and classification.

Overview of Text Analytics

• Definition

  Text Analytics, also known as text mining, is the process of extracting meaningful patterns and insights from large volumes of textual data using NLP and statistical methods.

• Core techniques

  Common techniques include tokenization, stemming, lemmatization, part-of-speech tagging, named entity recognition, and sentiment analysis.

  • Tokenization:

    Breaking text into words or phrases to prepare for analysis.

  • Stemming & lemmatization:

    Reducing words to their root forms to unify word variants.

  • Named entity recognition:

    Identifying proper nouns like people, organizations, or locations.

  • Sentiment analysis:

    Determining the emotional tone or polarity of text.

Applications in Analytics

• Customer feedback analysis

  Analyze reviews, surveys, and support tickets to gauge customer sentiment, discover pain points, and prioritize product improvements.

  • Use case example:

    Evaluating Net Promoter Score comments to identify frequently mentioned issues.

  • Metrics:

    Sentiment distribution, topic frequency, and sentiment trend lines over time.

• Social media monitoring

  Track brand mentions and trending topics across social platforms to understand audience perception and emerging discussions.

  • Tools:

    Integrations with Twitter, Facebook, and Instagram APIs for real-time data.

  • Outcomes:

    Detecting viral posts, sentiment shifts, and influencer impact.

• Content categorization

  Automatically classify documents, articles, or comments into predefined topics, tags, or sentiment buckets.

  • Methodology:

    Training supervised models on labeled datasets to assign categories.

  • Systems:

    News topic classification, spam detection, or support ticket routing.

Implementing Text Analytics with SaaS Tools

• Using plainsignal for text input events

  plainSignal offers a lightweight, cookie-free way to capture text-based user interactions—such as search queries or form submissions—while respecting privacy regulations.

  • Tracking script:

    Add this snippet to your HTML to enable plainSignal tracking:

    <link rel="preconnect" href="//eu.plainsignal.com/" crossorigin />
    <script defer data-do="yourwebsitedomain.com" data-id="0GQV1xmtzQQ" data-api="//eu.plainsignal.com" src="//cdn.plainsignal.com/PlainSignal-min.js"></script>
    

  • Event configuration:

    Configure custom events in the plainSignal dashboard to capture specific text fields, such as search queries or feedback messages.

• Configuring ga4 for text analytics

  Google Analytics 4 can record custom events containing text parameters, enabling basic text analytics on user interactions captured on your website.

  • Ga4 setup:

    Ensure you have the gtag.js snippet with your Measurement ID included in your pages. This initializes GA4 tracking.

  • Event tracking example:

    Use the following code to send a text input value as a custom event:

    gtag('event', 'text_submit', {
      'event_category': 'form',
      'event_label': 'message_input',
      'message': document.getElementById('contact_message').value
    });
    

Best Practices and Considerations

• Data privacy and compliance

  Protect user privacy by obtaining consent, anonymizing data, and adhering to regulations like GDPR and CCPA.

  • User consent:

    Obtain explicit permission before collecting or analyzing user-generated text.

  • Anonymization:

    Mask or remove personally identifiable information to safeguard user identities.

• Data preprocessing

  Clean and normalize text data to improve model accuracy and reduce noise before analysis.

  • Tokenization & cleaning:

    Split text into tokens and remove punctuation, HTML tags, or irrelevant characters.

  • Stop-word removal:

    Remove common words (e.g., ‘the’, ‘and’) that do not contribute meaningful information.

  • Normalization:

    Convert text to lowercase, handle accents, and expand contractions for consistency.

• Tool selection

  Choose platforms based on factors like scalability, integration capabilities, and depth of NLP features.

  • Scalability:

    Ensure the solution can handle your expected data volume and processing demands.

  • Integration:

    Verify compatibility with your existing data sources, BI tools, and workflow systems.

Related terms