ISEAR Dataset Sentiment Analysis

This is a Python script to preprocess, build, train and test the ISEAR dataset on sentiment analysis. It has implemented:

• a CNN model
• an LSTM model
• an MLP model used as the baseline model.

The architecture of the CNN model is the one described in the paper Convolutional Neural Networks for Sentence Classification (Kim, 2014), although the convolutional kernel sizes and the number of features could be altered.

The architecture of the LSTM model consists of an embedding layer, a (optionally) bidirectional LSTM layer and a fully connected layer. The number of stacked LSTM cells, the number of features in the hidden state, and whether or not to use bidirectional LSTM can be specified in command line arguments.

The MLP model consists of two hidden fully connected layers and is meant to be used as a baseline model.

Run train script

A simple command of

python train.py

will run the training script on the CNN model.

To specify which model to use, or other parameters, refer to the following table.

Parameter Name	Type	Default value	Description
--model	str	'cnn'	Which model to train. Currently, can choose between "cnn", "lstm", and "mlp".
--epoch	int	60	Number of epochs in training.
--batch_size	int	64	Batch size.
--data_dir	str	'./data'	Directory for train, validation, and test set csv files.
--emb_dim	int	300	Word embedding dimension.
--lr	float	0.5	Learning rate.
--weight_decay	float	0.01	Weight decay (L2 regularization) constant.
--gamma	float	0.1	Multiplicative factor of learning rate decay.
--step_size	float	15	Period of learning rate decay.
--dropout	float	0.5	During training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution.
--lstm_hidden_dim	int	64	Number of features in LSTM hidden state.
--lstm_num_layers	int	1	Number of recurrent layers of LSTM. E.g., setting num_layers=2 would mean stacking two LSTMs together to form a stacked LSTM, with the second LSTM taking in outputs of the first LSTM and computing the final results.
--lstm_bidirectional	bool	True	If True, becomes a bidirectional LSTM.
--cnn_num_features	int	200	Number of channels (features) produced by the convolution.
--cnn_window_sizes	list	[2, 3, 4, 5]	Size of the convolving kernel, or in 1d terms window size. To use multiple window sizes, run like so: python train.py --model cnn --cnn_window_sizes 2 3 4 5

Requirements

The following packages are required, and are given in requirements.txt:

torch==1.8.1
torchtext==0.9.1
tqdm
pandas
scikit-learn
matplotlib

Using the command

pip install -r requirements.txt

will install all these packages automatically.