Members: Yinghan Ma, Nicholas Miller, Yichong Ma, Gregory DeCanio
File Structure
Gregory
LSTM as Classifer Metrics.ipynb - Jupyter Notebook where code was written to output classifier metrics of LSTM, assuming we convert the regression values from the original LSTM to their appropriate toxic/non-toxic classification
TF_IDF Text Classification.ipynb - Jupyter Notebook where project code is written. Performs data cleaning (stop word and character removal). Uses TF_IDF and simple classification methods
README.md - Contains links to websites that were used as reference when coding
Nicholas
README.md - Contains link to Google Colab where project code is written
Yichong
__pycache__
wordcloud.cpython-37.pyc
AdditionalTarget.py
Histogram.py
PieChart.py
README.md
TargetFeature.py
WCObscene75.py
WCPopWords.py
WCInfo.py
unknownWordGraph.py
unknownWords.py
Yinghan
README.md
__init__.py
clean_data.py
data_utils.py
model.py
run.sh
task.py
weightpath.py
.gitignore - Tells git which files to ignore
README.md - This file! Describes problem, file structure, and what each group member is doing
new_data.csv - Sampled data of 115,000 comments from original 1.8+ million
requirement.txt
Visuals:
Method 1: Yichong
Analysis
Graphs
Stats
Method 2: Nick
Word2vec
Predictors:
Method 1: Yinghan
Transformer (Bert)
Method 2: Nick
LSTM
Method 3: Greg
Performing the following classification methods using TF_IDF.
Output accuracy, precision, recall, F1-Score, and time needed to construct model.