Gender Effect in social media platforms

Goal: study gender effect on perceived helpfuness of reviews depending on the gender of authors

Datasets

Place your dataset under the folder datasets/DATASETNAME where DATASETNAME is the folder name you give.
Clean your dataset by one of ipynb files with suffix Dataset.ipynb, obtaining datasets for training, test and validation.
Go to the folder models/HAN, executing preprocess.py to preprocess the datasets
Go to the folder models/GRU, executing RNN_model_batch.py to train the model. After the training is done, run inference.py to infer the gender labels for the undisclosed dataset.
Open MajorityVoting.ipynb, apply majority voting on predicted undisclosed dataset
Open SentimentReadabilityLengthCal.ipynb to analyze review's sentiment, length, readability. Four datasets produced, Signaling Man (SM), Signaling Woman (SW), Performing Man (PM), Performing Woman (PW)
Open NaturalExptCategory(not_pairwise).ipynb to find matches for each pair group of (SM, SW), (SM, PM), (SW, PW). After matches of each pair group found, analyzing the helpfuness score to get a conclusion.

Name		Name	Last commit message	Last commit date
Latest commit History 18 Commits
datasets/pan		datasets/pan
models		models
LICENSE		LICENSE
MajorityVoting.ipynb		MajorityVoting.ipynb
NaturalExptCategory_not_pairwise.ipynb		NaturalExptCategory_not_pairwise.ipynb
PanDataset_XmlToCsv.ipynb		PanDataset_XmlToCsv.ipynb
PropensityScoreMatch.ipynb		PropensityScoreMatch.ipynb
README.md		README.md
RedditDataset.ipynb		RedditDataset.ipynb
SentimentReadabilityLengthCal.ipynb		SentimentReadabilityLengthCal.ipynb
StackExchangeDataset.ipynb		StackExchangeDataset.ipynb
StatisticUsersReviews.ipynb		StatisticUsersReviews.ipynb
StatisticalLinguistics.ipynb		StatisticalLinguistics.ipynb
YelpDataset.ipynb		YelpDataset.ipynb