Sentiment Analysis Package

# TakeBlipSentimentAnalysis

Data & Analytics Research

## Overview

Sentiment analysis is the process of detecting the sentiment of a sentence, the sentiment could be negative, positive or neutral.

This implementation uses a LSTM implementation to solve the task. The implementation is using PyTorch framework and Gensim FastText as input embedding.

To train the model it is necessary a csv file with the labeled dataset, and an embedding file. For prediction, the files needed are the embedding model, the trained model and the vocabulary of the labels (output of the train).

This implementation presents the possibility to predict the sentiment for a single sentence and for a batch of sentences (by file or dictionary).

The LSTM architecture utilized in this implementation has 4 layers:

1. Embedding layer: a layer with the embedding representation of each word.

2. LSTM layer: receives as input the embedding representation of each word in a sentence. For each word generate a output with size pre-defined.

3. The linear output layer: receives as input the last word hidden output of the LSTM and applies a linear function to get a vector of the size of the possible labels.

4. Softmax layer: receives the output of the linear layer and apply softmax operation to get the probability of each label.

For the bidirectional LSTM the linear output layer receives the hidden output from the first and the last word.

## Training

To train your own Sentiment Analysis model you will need a csv file with the following structure:

Message		                              Sentiment
achei pessimo o atendimento	              Negative
otimo trabalho		                      Positive
bom dia                                       Neutral
...,						...


A few steps should be followed to train the model.

1. Import the main functions
2. Set the variables
3. Generate the vocabularies
4. Initialize the model
5. Training

An example with the steps

### Import main functions

import torch
import os
import pickle

from TakeSentimentAnalysis import model, utils
from TakeSentimentAnalysis.train import LSTMTrainer


### Set the variables

File variables

input_path = '*.csv'
sentence_column = 'Message'
label_column = 'Sentiment'
encoding = 'utf-8'
separator = '|'
use_pre_processing = True
save_dir = 'path_to_save_folder'
wordembed_path = '*.kv'


The file variables are:

• input-path: Path to input file that contains sequences of sentences.
• sentence_column: String with the name of the column of sentences to be read from input file.
• label_column: String with the name of the column of labels to be read from input file.
• encoding: Input file encoding.
• separator: Input file column separator.
• use_pre_processing: Whether to pre process input data
• save-dir: Directory to save outputs.
• wordembed-path: Path to pre-trained word embeddings.

Validation variables

val = True
val-path = '*.csv'
val-period = 1

• val: Whether to perform validation.
• val-path: Validation file path. Must follow the same structure of the input file.
• val-period: Period to wait until a validation is performed.

Model variables

word-dim = 300
lstm-dim = 300
lstm-layers = 1
dropout-prob = 0.05
bidirectional = False
epochs = 5
batch-size = 32
shuffle = False
learning-rate = 0.001
learning-rate-decay = 0.1
max-patience = 2
max-decay-num = 2
patience-threshold = 0.98

• word-dim: Dimension of word embeddings.
• lstm-dim: Dimensions of lstm cells. This determines the hidden state and cell state sizes.
• lstm-layers: Number of layers of the lstm cells.
• dropout-prob: Probability in dropout layers.
• bidirectional: Whether lstm cells are bidirectional.
• epochs: Number of training epochs.
• batch-size: Mini-batch size to train the model.
• shuffle: Whether to shuffle the dataset.
• learning-rate: Learning rate to train the model.
• learning-rate-decay: Learning rate decay after the model not improve.
• max-patience: Number maximum of epochs accept with decreasing loss in validation, before reduce the learning rate.
• max-decay-num: Number maximum of times that the learning can be reduced.
• patience-threshold: Threshold of the loss in validation to be considered that the model didn't improve.

### Generate the vocabularies

Generate the sentences vocabulary. This steps is necessary to generate a index to each word in the sentences (on train and validation datasets) to retrieve information after PyTorch operations.

    pad_string = '<pad>'
unk_string = '<unk>'
sentence_vocab = vocab.create_vocabulary(
input_path=input_path,
column_name=sentence_column,
unk_string=unk_string,
encoding=encoding,
separator=separator,
use_pre_processing=use_pre_processing)

if val:
path=val_path,
column=sentence_column,
encoding=encoding,
separator=separator,
use_pre_processing=use_pre_processing)
vocab.populate_vocab(sentences, sentence_vocab)


Generating the labels vocabulary. To generate a index of each label, this object is necessary to predict, so must be saved.

    label_vocab = vocab.create_vocabulary(
input_path=input_path,
column_name=label_column,
unk_string=unk_string,
encoding=encoding,
separator=separator,
is_label=True)
vocab_label_path = os.path.join(save_dir,
'vocab-label.pkl')
pickle.dump(label_vocab, open(vocab_label_path, 'wb'))


### Initialize the model

Initialize the LSTM model.

    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
lstm_model = model.LSTM(
vocab_size=len(sentence_vocab),
word_dim=word_dim,
n_labels=len(label_vocab),
hidden_dim=lstm_dim,
layers=lstm_layers,
dropout_prob=dropout_prob,
device=device,
bidirectional=bidirectional
).to(device)

lstm_model.reset_parameters()



Fill the embedding layer with the representation of each word in the vocabulary.

    wordembed_path = wordembed_path
lstm_model.embeddings[0].weight.data = torch.from_numpy(
fasttext[sentence_vocab.i2f.values()])


### Training

    trainer = LSTMTrainer(
lstm_model=lstm_model,
epochs=epochs,
input_vocab=sentence_vocab,
input_path=input_path,
label_vocab=label_vocab,
save_dir=save_dir,
val=val,
val_period=val_period,
unk_string=unk_string,
batch_size=batch_size,
shuffle=shuffle,
label_column=label_column,
encoding=encoding,
separator=separator,
use_pre_processing=use_pre_processing,
learning_rate=learning_rate,
learning_rate_decay=learning_rate_decay,
max_patience=max_patience,
max_decay_num=max_decay_num,
patience_threshold=patience_threshold,
val_path=val_path)
trainer.train()



## Prediction

The prediction can be made for a single sentence or for a batch of sentences.

In both cases a few steps should be followed.

1. Import the main functions
2. Set the variables
3. Initialize the model
4. Predict

### Import main functions

import sys
import os
import torch

from TakeSentimentAnalysis import utils
from TakeSentimentAnalysis.predict import SentimentPredict


### Set the variables

model_path = '*.pkl'
label_vocab = '*.pkl'
save_dir = '*.csv'
encoding = 'utf-8'
separator = '|'

• model_path: Path to trained model.
• label_vocab: Path to input file that contains the label vocab.
• save_dir: Directory to save predict.
• encoding: Input file encoding.
• separator: Input file column separator.

### Initialize the model

sys.path.insert(0, os.path.dirname(model_path))

unk_string = '<unk>'

SentimentPredicter = SentimentPredict(model=lstm_model,
label_path=label_vocab,
embedding=embedding,
save_dir=save_dir,
encoding=encoding,
separator=separator)


### Single Prediction

To predict a single sentence

SentimentPredicter.predict_line(line=sentence)


### Batch Prediction

To predict in a batch a few more variables are need:

• batch_size: Mini-batch size.
• shuffle: Whether to shuffle the dataset.
• use_pre_processing: Whether to pre-processing the input data.

To predict a batch using dictionary:

SentimentPredicter.predict_batch(
filepath='',
sentence_column='',
unk_string=unk_string,
batch_size=batch_size,
shuffle=shuffle,
use_pre_processing=use_pre_processing,
sentences=[{'id': 1, 'sentence': sentence_1},
{'id': 2, 'sentence': sentence_2}]))


To predict a batch using a csv file:

SentimentPredicter.predict_batch(
filepath=input_path,
sentence_column=sentence_column,
unk_string=unk_string,
batch_size=batch_size,
shuffle=shuffle,
use_pre_processing=use_pre_processing)

• input_path: Path to the input file containing the sentences to be predicted.
• sentence_column: String with the name of the column of sentences to be read from input file.

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