I’ve got a huge collection of journal entries spanning 10 years. It’s all in Google Docs and it’s a lot of text. I’m looking for an AI tool that can dig deep into this stuff and give me some real insights.
I tried using ChatGPT. It’s okay when I ask about specific things like how my feelings of loneliness changed from 2015 to 2025. But when I ask bigger questions like how I’ve grown over the years, it doesn’t do so well. It tends to focus on just a few months or give really general answers.
Does anyone know a better way to get AI to analyze all this text? I’m after something that can handle broad questions across a big chunk of writing. Maybe there’s a special tool or technique I should be using?
Here’s a quick example of what I’ve tried:
def analyze_journal(entries, topic):
insights = []
for entry in entries:
if topic in entry:
insights.append(extract_insight(entry, topic))
return summarize(insights)
# But this doesn't work well for broader analysis
Any ideas on how to improve this or what else I could try?
yo, have u tried using some machine learning stuff? like, maybe a tool that can handle loads of text and spot patterns over time. there’s this thing called ‘deep learning’ that might work for ur journals. it can pick up on subtle changes n stuff. just a thought, might be worth checkin out!
Have you considered using a Natural Language Processing (NLP) tool specifically designed for longitudinal text analysis? Something like LIWC (Linguistic Inquiry and Word Count) might be more suitable for your needs. It’s designed to analyze large volumes of text over time and can provide insights into psychological states, emotional trends, and personal growth patterns.
For a more technical approach, you could look into topic modeling techniques like Latent Dirichlet Allocation (LDA). This could help identify recurring themes across your journal entries and how they’ve evolved over the years. You might also want to explore sentiment analysis tools to track emotional changes over time.
If you’re comfortable with coding, libraries like NLTK or spaCy in Python offer powerful text analysis capabilities. You could create a custom pipeline that processes your entries chronologically, extracting key themes, sentiment, and personal growth indicators.
Remember, the key is to pre-process your data effectively and choose the right analytical methods for the insights you’re seeking. It might take some experimentation to find the right approach for your specific journal entries.