By Bocaj Mai in NLP for Marketers — Jul 27, 2024

Thematic segmentation with NLP and Python (for a voice journal)

Natural Language Processing (NLP) offers powerful tools for analyzing and understanding human language. This article explores the application of NLP techniques in building a processing pipeline for voice journal transcripts.

The goal is to segment these transcripts into coherent units, preparing them for high-quality classification in subsequent steps of analysis.

The Problem: Segmenting Voice Journal Transcripts

Voice journaling allows for a natural, stream-of-consciousness form of expression. However, the resulting transcripts often lack clear structure, making them challenging to analyze systematically. Our task is to develop a pipeline that can take these unstructured transcripts and break them into meaningful segments.

The ultimate aim is to improve the quality of downstream classification tasks, which might involve large language models or other advanced NLP techniques. By providing well-structured input, we can enhance the accuracy and relevance of these subsequent analyses.

Setting Up the Environment

Before diving into NLP tasks, it's crucial to set up a proper Python environment. This ensures that our project has the necessary dependencies and remains isolated from other Python projects.

Install the required libraries:

pip install nltk scikit-learn gensim

Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows, use: venv\Scripts\activate

Create a new project directory and navigate to it:

mkdir voice_journal_nlp
cd voice_journal_nlp

Initial NLP Steps: Sentence Tokenization

Our first task in processing the transcripts is to break them into sentences. This forms the foundation for more advanced segmentation later. We'll use the Natural Language Toolkit (NLTK) for this purpose.

Here's a script that demonstrates basic sentence tokenization:

import nltk
from nltk.tokenize import sent_tokenize

def main():
    # Download necessary NLTK data
    nltk.download('punkt')

    # Sample transcript text
    transcript = "Hey this is my voice journal for today. I had a pretty good day overall. Work was challenging but I managed to solve a tough problem. Later I went for a run in the park. It was refreshing and helped clear my mind."

    # Tokenize the text into sentences
    sentences = sent_tokenize(transcript)

    print("Original transcript:")
    print(transcript)
    print("\nSentences:")
    for i, sentence in enumerate(sentences, 1):
        print(f"{i}. {sentence}")

if __name__ == "__main__":
    main()

Let's break down this script:

We import NLTK and its sentence tokenizer.
The nltk.download('punkt') line downloads the necessary data for the Punkt sentence tokenizer, which uses an unsupervised algorithm to build a model for abbreviation words, collocations, and words that start sentences.
We define a sample transcript.
sent_tokenize() splits our text into a list of sentences.
We print the original text and then each sentence separately.

When you run this script (python sentence_tokenize.py), you should see output like this:

Original transcript:
Hey this is my voice journal for today. I had a pretty good day overall. Work was challenging but I managed to solve a tough problem. Later I went for a run in the park. It was refreshing and helped clear my mind.

Sentences:
1. Hey this is my voice journal for today.
2. I had a pretty good day overall.
3. Work was challenging but I managed to solve a tough problem.
4. Later I went for a run in the park.
5. It was refreshing and helped clear my mind.

This basic tokenization works well for standard text, but voice journals present unique challenges. Speech patterns often include run-on sentences, incomplete thoughts, and other irregularities that can confuse standard tokenization methods. This is why we need to explore more advanced techniques.

Advancing the Pipeline: Thematic Segmentation

To create more meaningful segments from our voice journal transcripts, we need to go beyond simple sentence tokenization. One approach is to use thematic segmentation, which aims to group sentences or phrases that discuss the same topic.

A popular technique for this is TextTiling, developed by Marti Hearst in 1997. TextTiling works by:

Tokenizing the text into pseudo-sentences of a fixed size (usually 20 words).
Computing the similarity between adjacent blocks of pseudo-sentences.
Identifying boundaries where the similarity between blocks is low, indicating a potential topic shift.

Here's an implementation using NLTK's TextTiling algorithm:

import nltk
from nltk.tokenize import sent_tokenize
from nltk.tokenize.texttiling import TextTilingTokenizer

def main():
    nltk.download('punkt')
    nltk.download('stopwords')

    transcript = """
    Hey this is my voice journal for today. I had a pretty good day overall. Work was challenging but I managed to solve a tough problem. I felt really accomplished after that. Later I went for a run in the park. It was refreshing and helped clear my mind. The weather was perfect for running. I saw a lot of people out enjoying the day. After the run, I made dinner and watched a movie. The movie was a sci-fi thriller that really kept me on the edge of my seat. I'm looking forward to tomorrow. I have a meeting with my team to discuss our new project. I hope it goes well.

    Now, let me talk about my plans for the weekend. I'm thinking of going hiking in the nearby mountains. The forecast looks good, and I've been wanting to try out this new trail I heard about. I'll probably pack a lunch and make a day of it. Maybe I'll invite some friends along too. It would be nice to spend some time in nature and catch up with people.

    On Sunday, I'm planning to do some meal prep for the week. I've been trying to eat healthier lately, so I want to prepare some nutritious meals in advance. I'm thinking of making a big batch of vegetable soup, some grilled chicken, and maybe some quinoa salad. It takes some time, but it really helps me stay on track with my diet during the busy workweek.

    Speaking of health, I've been doing pretty well with my exercise routine lately. Besides the running, I've been doing some strength training at home. I found some great workout videos online that don't require any equipment. It's amazing how much you can do with just bodyweight exercises. I've noticed I have more energy throughout the day when I stick to my workout routine.

    Lastly, I've been thinking about starting a new hobby. I've always been interested in photography, and I think it might be fun to learn more about it. I've been researching different cameras and techniques online. Maybe I'll sign up for a beginner's class or join a local photography club. It would be a great way to meet new people and explore my creative side.
    """

    # Sentence tokenization
    sentences = sent_tokenize(transcript)

    # TextTiling for thematic segmentation
    tt = TextTilingTokenizer()
    segments = tt.tokenize(transcript)

    print("Thematic Segments:")
    for i, segment in enumerate(segments, 1):
        print(f"\nSegment {i}:")
        print(segment)

if __name__ == "__main__":
    main()

💡

Note: this will not work if your input is too short, keep that in mind when using your own transcript.

When you run this script (python thematic_segment.py), you'll see the transcript divided into thematic segments. Each segment should roughly correspond to a distinct topic or theme in the journal entry.

For my example, it returned:

Thematic Segments:

Segment 1:

    Hey this is my voice journal for today. I had a pretty good day overall. Work was challenging but I managed to solve a tough problem. I felt really accomplished after that. Later I went for a run in the park. It was refreshing and helped clear my mind. The weather was perfect for running. I saw a lot of people out enjoying the day. After the run, I made dinner and watched a movie. The movie was a sci-fi thriller that really kept me on the edge of my seat. I'm looking forward to tomorrow. I have a meeting with my team to discuss our new project. I hope it goes well.

Segment 2:


    Now, let me talk about my plans for the weekend. I'm thinking of going hiking in the nearby mountains. The forecast looks good, and I've been wanting to try out this new trail I heard about. I'll probably pack a lunch and make a day of it. Maybe I'll invite some friends along too. It would be nice to spend some time in nature and catch up with people.

Segment 3:


    On Sunday, I'm planning to do some meal prep for the week. I've been trying to eat healthier lately, so I want to prepare some nutritious meals in advance. I'm thinking of making a big batch of vegetable soup, some grilled chicken, and maybe some quinoa salad. It takes some time, but it really helps me stay on track with my diet during the busy workweek.

Segment 4:


    Speaking of health, I've been doing pretty well with my exercise routine lately. Besides the running, I've been doing some strength training at home. I found some great workout videos online that don't require any equipment. It's amazing how much you can do with just bodyweight exercises. I've noticed I have more energy throughout the day when I stick to my workout routine.

Segment 5:


    Lastly, I've been thinking about starting a new hobby. I've always been interested in photography, and I think it might be fun to learn more about it. I've been researching different cameras and techniques online. Maybe I'll sign up for a beginner's class or join a local photography club. It would be a great way to meet new people and explore my creative side.

TextTiling is particularly suitable for voice journals because:

It can identify topic shifts without relying on perfect sentence structure.
It's robust to variations in segment length, which is common in free-form speech.
It doesn't require pre-defined topics, making it adaptable to the diverse content of personal journals.

Comparing Segmentation Techniques

While TextTiling is effective, it's worth considering other approaches:

Topic Modeling (e.g., Latent Dirichlet Allocation):
- Pros: Can identify overarching themes across multiple entries.
- Cons: Requires specifying the number of topics in advance; can be computationally expensive.
Semantic Similarity-Based Approaches:
- Pros: Can capture nuanced relationships between sentences.
- Cons: Depends heavily on the quality of word embeddings; may struggle with domain-specific vocabulary.
TextTiling:
- Pros: Works well with stream-of-consciousness text; doesn't require pre-defined topics.
- Cons: May struggle with very short segments or subtle topic transitions.

For voice journals, TextTiling often provides a good balance of effectiveness and computational efficiency. However, as we refine our pipeline, we might consider hybrid approaches that combine multiple techniques.

Evaluating Segmentation Quality

Assessing the quality of our segmentation is crucial for ensuring that it actually improves downstream classification tasks. Here are some evaluation strategies:

Manual Annotation: Have human annotators mark topic boundaries in a sample of transcripts. Compare these to the algorithm's output using metrics like Pk or WindowDiff, which measure segmentation accuracy.
Intrinsic Evaluation: Measure the coherence of the segments using metrics like topic coherence or word embedding similarity within segments versus between segments.
Extrinsic Evaluation: Assess how the segmentation affects the performance of downstream tasks. For example, compare the accuracy of sentiment analysis or topic classification on segmented versus unsegmented text.
A/B Testing: If possible, implement multiple segmentation methods in your pipeline and compare their impact on the final output of your analysis.

Ethical Considerations

Processing personal journal entries raises important ethical considerations:

Privacy: Ensure that all processing happens locally on the user's device to prevent unauthorized access to personal data.
Transparency: Clearly communicate to users how their journal entries are being processed and what insights are being derived.
User Control: Provide options for users to opt out of certain types of analysis or to delete their data.
Bias: Be aware of potential biases in your segmentation and classification algorithms, especially when dealing with diverse populations.
Mental Health Implications: If your analysis touches on sensitive topics like mental health, consider involving experts in psychology or counseling to ensure responsible handling of the data.

Challenges and Next Steps

While we've made progress in segmenting voice journal transcripts, several challenges remain:

Speech Patterns: Voice journals often contain disfluencies, repeated words, and incomplete sentences. Future work could focus on developing preprocessing steps to normalize these speech patterns.
Context Preservation: Ensure that the segmentation doesn't break apart contextually related information. This might involve implementing a sliding window approach or adding a post-processing step to merge closely related segments.
Scalability: As the volume of transcripts grows, optimize the processing pipeline for efficiency. This could involve parallelizing the segmentation process or using incremental learning techniques.
Multimodal Analysis: Incorporate prosodic features from the original audio, such as tone and pacing, to improve segmentation accuracy.
Personalization: Develop techniques to adapt the segmentation algorithm to individual users' speech patterns and journaling styles.

To address these challenges, consider the following next steps:

Implement custom tokenization rules that account for common speech patterns in voice journals.
Explore more advanced segmentation techniques, such as hierarchical topic modeling or neural network-based approaches.
Develop a robust evaluation framework that combines intrinsic and extrinsic measures of segmentation quality.
Experiment with incorporating audio features to create a truly multimodal analysis pipeline.
Investigate techniques for continual learning, allowing the system to improve its segmentation over time based on user feedback and more data.

Conclusion

Building a processing pipeline for voice journal transcripts is a complex but rewarding challenge. By starting with basic sentence tokenization and advancing to thematic segmentation, we've laid the groundwork for more sophisticated analysis.

As we continue to refine this pipeline, we'll not only improve our ability to derive insights from voice journals but also contribute to the broader field of NLP for personal analytics. The ultimate goal is to create tools that help individuals gain meaningful insights from their personal reflections while respecting their privacy and agency.

The journey from raw voice transcripts to structured, analyzable data is just beginning. Each step forward opens new possibilities for understanding the rich, complex tapestry of human thought captured in our daily reflections.