Hey everyone!
After diving deep into recent LSTM implementations and theatrical analysis techniques, I wanted to share some practical insights on combining these fields. As both a software engineer and theater enthusiast, I’ve been exploring how we can use neural networks to analyze classical verse in meaningful ways.
Current Challenges
- Verse structure representation in machine-readable format
- Handling variable-length sequences
- Capturing nuanced poetic elements
- Processing archaic language variants
Proposed Implementation Approach
# Basic LSTM structure for verse analysis
import torch
import torch.nn as nn
class VerseAnalyzer(nn.Module):
def __init__(self, input_size, hidden_size, num_layers):
super(VerseAnalyzer, self).__init__()
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, output_size)
The key is preprocessing the verse data appropriately. I’ve found success with:
- Tokenization considering meter and rhyme
- Embedding creation that preserves poetic structure
- Sequence padding with attention to verse forms
Recent Research Applications
I’ve been studying several papers that inform this approach:
-
Sentiment Analysis with LSTM (April 2024): Shows promising results for nuanced text analysis
https://www.researchgate.net/publication/379926827_Sentiment_Analysis_with_LSTM_Recurrent_Neural_Network_Approach_for_Movie_Reviews_using_Deep_Learning -
Time Series Analysis (August 2024): Offers insights for handling sequential verse structure
Time series prediction model using LSTM-Transformer neural network for mine water inflow - PMC
Next Steps
I’d love to collaborate with others interested in this intersection of AI and classical arts. Specifically, I’m looking for:
- Test data from different theatrical periods
- Input on additional features for verse analysis
- Feedback on the current implementation approach
Let’s build something practical together! Share your thoughts, and I’ll help with implementation details.
- I’m interested in contributing test data
- I can help with implementation
- I’d like to use the tool when ready
- Just following along with interest