# Twitter Sentiment Analysis¶

By Neuromatch Academy

Content creators: Juan Manuel Rodriguez, Salomey Osei, Gonzalo Uribarri

Production editors: Amita Kapoor, Spiros Chavlis

Our 2021 Sponsors, including Presenting Sponsor Facebook Reality Labs

# Step 1: Questions and goals¶

• Can we infer emotion from a tweet text?

• How words are distributed accross the dataset?

• Are words related to one kind of emotion?

# Step 2: Literature review¶

Original Dataset Paper

Papers with code

# Step 3: Load and explore the dataset¶

## Install dependencies¶

# @title Install dependencies
!pip install pandas --quiet
!pip install torchtext --quiet

# We import some libraries to load the dataset
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from collections import Counter
from tqdm.notebook import tqdm

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import TensorDataset, DataLoader

import torchtext
from torchtext.data import get_tokenizer

from sklearn.utils import shuffle
from sklearn.metrics import classification_report
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer


You can find the dataset we are going to use in this website.

import requests, zipfile, io
url = 'http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip'
r = requests.get(url)
z = zipfile.ZipFile(io.BytesIO(r.content))
z.extractall()

# We load the dataset
header_list = ["polarity", "id", "date", "query", "user", "text"]
df = pd.read_csv('training.1600000.processed.noemoticon.csv',
encoding = "ISO-8859-1", names=header_list)

# Let's have a look at it
df.head()

polarity id date query user text
0 0 1467810369 Mon Apr 06 22:19:45 PDT 2009 NO_QUERY _TheSpecialOne_ @switchfoot http://twitpic.com/2y1zl - Awww, t...
1 0 1467810672 Mon Apr 06 22:19:49 PDT 2009 NO_QUERY scotthamilton is upset that he can't update his Facebook by ...
2 0 1467810917 Mon Apr 06 22:19:53 PDT 2009 NO_QUERY mattycus @Kenichan I dived many times for the ball. Man...
3 0 1467811184 Mon Apr 06 22:19:57 PDT 2009 NO_QUERY ElleCTF my whole body feels itchy and like its on fire
4 0 1467811193 Mon Apr 06 22:19:57 PDT 2009 NO_QUERY Karoli @nationwideclass no, it's not behaving at all....

For this project we will use only the text and the polarity of the tweet. Notice that polarity is 0 for negative tweets and 4 for positive tweet.

X = df.text.values

# Changes values from [0,4] to [0,1]
y = (df.polarity.values > 1).astype(int)

# Split the data into train and test
x_train_text, x_test_text, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42,stratify=y)


The first thing we have to do before working on the models is to familiarize ourselves with the dataset. This is called Exploratory Data Analisys (EDA).

for s, l in zip(x_train_text[:5], y_train[:5]):
print('{}: {}'.format(l, s))

1: @paisleypaisley LOL why do i get ideas so far in advance? it's not even june yet! we need a third knitter to have our own summer group
0: worst headache ever
0: @ewaniesciuszko  i am so sad i wont see you! I miss you already. and yeah! that's perfect; i come back the 18th!
1: doesn't know how to spell conked
0: &quot;So we stand here now and no one knows us at all I won't get used to this I won't get used to being gone&quot;...I miss home and everyone  -a


An interesting thing to analyze is the Word Distribution. In order to count the occurrences of each word, we should tokenize the sentences first.

tokenizer = get_tokenizer("basic_english")

print('Before Tokenize: ', x_train_text[1])
print('After Tokenize: ', tokenizer(x_train_text[1]))

Before Tokenize:  worst headache ever
After Tokenize:  ['worst', 'headache', 'ever']

x_train_token = [tokenizer(s) for s in tqdm(x_train_text)]
x_test_token = [tokenizer(s) for s in tqdm(x_test_text)]






We can count the words occurences and see how many different words are present in our dataset.

words = Counter()
for s in x_train_token:
for w in s:
words[w] += 1

sorted_words = list(words.keys())
sorted_words.sort(key=lambda w: words[w], reverse=True)
print(f"Number of different Tokens in our Dataset: {len(sorted_words)}")
print(sorted_words[:100])

Number of different Tokens in our Dataset: 669284
['.', 'i', '!', "'", 'to', 'the', ',', 'a', 'my', 'it', 'and', 'you', '?', 'is', 'for', 'in', 's', 'of', 't', 'on', 'that', 'me', 'so', 'have', 'm', 'but', 'just', 'with', 'be', 'at', 'not', 'was', 'this', 'now', 'can', 'good', 'up', 'day', 'all', 'get', 'out', 'like', 'are', 'no', 'go', 'http', '-', 'today', 'do', 'too', 'your', 'work', 'going', 'love', 'we', 'got', 'what', 'lol', 'time', 'back', 'from', 'u', 'one', 'will', 'know', 'about', 'im', 'really', 'don', 'am', 'had', ')', 'see', 'some', 'there', 'its', '&amp', 'how', 'if', 'still', 'they', '&quot', 'night', '(', 'well', 'want', 'new', 'think', '2', 'home', 'thanks', 'll', 'oh', 'when', 'as', 'he', 'more', 'here', 'much', 'off']


Now we can plot their distribution.

count_occurences = sum(words.values())

accumulated = 0
counter = 0

while accumulated < count_occurences * 0.8:
accumulated += words[sorted_words[counter]]
counter += 1

print(f"The {counter * 100 / len(words)}% most common words "
f"account for the {accumulated * 100 / count_occurences}% of the occurrences")

The 0.13970153178620734% most common words account for the 80.00532743602652% of the occurrences

plt.bar(range(100), [words[w] for w in sorted_words[:100]])
plt.show()


It is very common to find this kind of distribution when analyzing corpus of text. This is referred to as the zipf’s law.

Usually the number of words in the dictionary will be very large.

Here are some thing we can do to reduce that number:

• Remove puntuation.

• Remove stop-words.

• Steaming.

• Remove very uncommon words (the words that appears in fewer than N occations).

• Nothing: we can use a pretrain model that handles this kind of situations.

We used one of the simplest tokenizers availables. This tokenizer does not take into account many quirks of the language. Moreover, diferent languages have different quirks, so there is no “universal” tokenizers. There are many libraries that have “better” tokenizers:

• Spacy: it can be accessed using: get_tokenizer("spacy"). Spacy supports a wide range of languages.

• Huggingface: it has many tokenizers for different laguages. Doc

• NLTK: it provides several tokenizers. One of them can be accessed using: get_tokenizer("toktok")

# Step 4: choose toolkit¶

Our goal is to train a model capable of estimating the sentiment of a tweet (positive or negative) by reading its content. To that end we will try 2 different approaches:

• A logistic regression using sklearn. NOTE: it can probaly work better than an SVM model.

• A simple Embedding + RNN.

## Logistic regression¶

We will represent our senteces using binary vectorization. This means that our data would be represented as a matrix of instances by word with a one if the word is in the instance, and zero otherwise. Sklean vectorizers can also do things such as stop-word removal and puntuation removal, you can read more about in the documentation.

vectorizer = CountVectorizer(binary=True)
x_train_cv = vectorizer.fit_transform(x_train_text)
x_test_cv = vectorizer.transform(x_test_text)

print('Before Vectorize: ', x_train_text[3])

Before Vectorize:  doesn't know how to spell conked

# Notice that the matriz is sparse
print('After Vectorize: ')
print(x_train_cv[3])

After Vectorize:
(0, 528584)	1
(0, 165468)	1
(0, 300381)	1
(0, 242211)	1
(0, 489893)	1
(0, 134160)	1


Now we can train our model. You can check the documentation of this logistic regressor here.

model = LogisticRegression(solver='saga')
model.fit(x_train_cv, y_train)

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='auto', n_jobs=None, penalty='l2',
random_state=None, solver='saga', tol=0.0001, verbose=0,
warm_start=False)

y_pred = model.predict(x_test_cv)

print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

0       0.81      0.79      0.80    160000
1       0.79      0.81      0.80    160000

accuracy                           0.80    320000
macro avg       0.80      0.80      0.80    320000
weighted avg       0.80      0.80      0.80    320000


## Explainable AI¶

The best thing about logistic regresion is that it is simple, and we can get some explanations.

print(model.coef_.shape)
print(len(vectorizer.vocabulary_))

words_sk = list(vectorizer.vocabulary_.keys())
words_sk.sort(key=lambda w: model.coef_[0, vectorizer.vocabulary_[w]])

(1, 589260)
589260

for w in words_sk[:20]:
print('{}: {}'.format(w, model.coef_[0, vectorizer.vocabulary_[w]]))

roni: -3.862597673594883
inaperfectworld: -3.5734362290886375
dontyouhate: -3.500197620227523
xbllygbsn: -3.412645372640648
anqju: -3.336405291553548
sad: -3.200522312464158
pakcricket: -3.1949158120163412
condolences: -3.132498019366488
heartbreaking: -3.066508733796654
saddest: -3.041999809733714
sadd: -3.029070563580306
heartbroken: -3.0287688233900174
boohoo: -3.022608649696793
sadface: -2.9918411285807234
rachelle_lefevr: -2.925057253107806
disappointing: -2.902524113779547
lvbu: -2.894705935001672
saddens: -2.8855127179984654
bummed: -2.83650014970307
neda: -2.792944556837498

for w in reversed(words_sk[-20:]):
print('{}: {}'.format(w, model.coef_[0, vectorizer.vocabulary_[w]]))

iamsoannoyed: 2.8494314732277672
myfax: 2.797451563471618
jennamadison: 2.5667257393706113
yeyy: 2.478028598852801
tryout: 2.4383315790116677
goldymom: 2.4374026022205535
wooohooo: 2.40297322137544
thesupergirl: 2.3565118467330004
iammaxathotspot: 2.311648368632618
londicreations: 2.3074490293400993
smilin: 2.2991891636718216
worries: 2.2899429774914717
sinfulsignorita: 2.2798963640981817
finchensnail: 2.264302079155878
smackthis: 2.2376679263761083
kv: 2.2158393907798413
tojosan: 2.211784259253832
russmarshalek: 2.2095374025599384
traciknoppe: 2.1768297770350835
congratulations: 2.171590496227557


What does this mean?

Remember the model.coef_ is the $$W$$ in:

$h(x)=\sigma(WX + b)$

where the label 1 is a positive tweet and the label 0 is a negative tweet.

## Recurrent Neural Network with Pytorch¶

In the previous section we use a Bag-Of-Words approach to represent each of the tweets. That meas that we only consider how many times each of the words appear in each of the tweets, we didnt take into account the order of the words. But we know that the word order is very important and carries relevant information.

In this section we will solve the same task, but this time we will implement a Recurrent Neural Network (RNN) instead of using a simple Logistic Regression.Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences.

Let’s start by importing the relevant libraries.

def set_device():
device = "cuda" if torch.cuda.is_available() else "cpu"
if device != "cuda":
print("WARNING: For this notebook to perform best, "
"if possible, in the menu under Runtime -> "
"Change runtime type.  select GPU ")
else:
print("GPU is enabled in this notebook.")

return device

# Set the device (check if gpu is available)
device = set_device()

GPU is enabled in this notebook.


First we will create a Dictionary (word_to_idx). This dictionary will map each Token (usually words) to an index (an integer number). We want to limit our dictionary to a certain number of tokens (num_words_dict), so we will include in our ditionary those with more occurrences.

# From previous section, we have a list with the most used tokens
sorted_words[:10]

['.', 'i', '!', "'", 'to', 'the', ',', 'a', 'my', 'it']


Let’s select only the most used.

num_words_dict = 30000
# We reserve two numbers for special tokens.
most_used_words = sorted_words[:num_words_dict-2]


We will add two extra Tokens to the dictionary, one for words outside the dictionary ('UNK') and one for padding the sequences ('PAD').

# dictionary to go from words to idx
word_to_idx = {}
# dictionary to go from idx to words (just in case)
idx_to_word = {}

# We include the special tokens first
PAD_token = 0
UNK_token = 1

word_to_idx['PAD'] = PAD_token
word_to_idx['UNK'] = UNK_token

idx_to_word[PAD_token] = 'PAD'
idx_to_word[UNK_token] = 'UNK'

# We popullate our dictionaries with the most used words
for num,word in enumerate(most_used_words):
word_to_idx[word] = num + 2
idx_to_word[num+2] = word


Our goal now is to transform each tweet from a sequence of tokens to a sequence of indexes. These sequences of indexes will be the input to our pytorch model.

# A function to convert list of tokens to list of indexes
def tokens_to_idx(sentences_tokens,word_to_idx):
sentences_idx = []
for sent in sentences_tokens:
sent_idx = []
for word in sent:
if word in word_to_idx:
sent_idx.append(word_to_idx[word])
else:
sent_idx.append(word_to_idx['UNK'])
sentences_idx.append(sent_idx)
return sentences_idx

x_train_idx = tokens_to_idx(x_train_token,word_to_idx)
x_test_idx = tokens_to_idx(x_test_token,word_to_idx)

some_number = 1
print('Before converting: ', x_train_token[some_number])
print('After converting: ', x_train_idx[some_number])

Before converting:  ['worst', 'headache', 'ever']
After converting:  [721, 458, 237]


We need all the sequences to have the same length. To select an adequate sequence length, let’s explore some statistics about the length of the tweets:

tweet_lens = np.asarray([len(sentence) for sentence in x_train_idx])
print('Max tweet word length: ',tweet_lens.max())
print('Mean tweet word length: ',np.median(tweet_lens))
print('99% percent under: ',np.quantile(tweet_lens,0.99))

Max tweet word length:  229
Mean tweet word length:  15.0
99% percent under:  37.0


We cut the sequences which are larger than our chosen maximum length (max_lenght) and fill with zeros the ones that are shorter.

 # We choose the max length
max_length = 40

# A function to make all the sequence have the same lenght
# Note that the output is a Numpy matrix
def padding(sentences, seq_len):
features = np.zeros((len(sentences), seq_len),dtype=int)
for ii, tweet in enumerate(sentences):
len_tweet = len(tweet)
if len_tweet != 0:
if len_tweet <= seq_len:
# If its shorter, we fill with zeros (the padding Token index)
features[ii, -len(tweet):] = np.array(tweet)[:seq_len]
if len_tweet > seq_len:
# If its larger, we take the last 'seq_len' indexes
features[ii, :] = np.array(tweet)[-seq_len:]
return features

# We convert our list of tokens into a numpy matrix
# where all instances have the same lenght
x_train_pad = padding(x_train_idx,max_length)
x_test_pad = padding(x_test_idx,max_length)

# We convert our target list a numpy matrix
y_train_np = np.asarray(y_train)
y_test_np = np.asarray(y_test)

some_number = 2
print('Before padding: ', x_train_idx[some_number])
print('After padding: ', x_train_pad[some_number])

Before padding:  [1, 3, 71, 24, 122, 3, 533, 74, 13, 4, 3, 102, 13, 209, 2, 12, 150, 4, 22, 5, 18, 667, 3, 138, 61, 7, 3296, 4]
After padding:  [   0    0    0    0    0    0    0    0    0    0    0    0    1    3
71   24  122    3  533   74   13    4    3  102   13  209    2   12
150    4   22    5   18  667    3  138   61    7 3296    4]


Now, let’s convert the data to pytorch format.

# create Tensor datasets
train_data = TensorDataset(torch.from_numpy(x_train_pad), torch.from_numpy(y_train_np))
valid_data = TensorDataset(torch.from_numpy(x_test_pad), torch.from_numpy(y_test_np))

# Batch size (this is an important hyperparameter)
batch_size = 100

# dataloaders
# make sure to SHUFFLE your data
train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size,drop_last = True)
valid_loader = DataLoader(valid_data, shuffle=True, batch_size=batch_size,drop_last = True)


Each batch of data in our traning proccess will have the folllowing format:

# Obtain one batch of training data
dataiter = iter(train_loader)
sample_x, sample_y = dataiter.next()

print('Sample input size: ', sample_x.size()) # batch_size, seq_length
print('Sample input: \n', sample_x)
print('Sample input: \n', sample_y)

Sample input size:  torch.Size([100, 40])
Sample input:
tensor([[    0,     0,     0,  ...,     4,     4,     4],
[    0,     0,     0,  ...,  7447, 14027,     2],
[    0,     0,     0,  ...,   100, 22241,     4],
...,
[    0,     0,     0,  ...,  2702,  4409,     2],
[    0,     0,     0,  ...,   162,    17,     1],
[    0,     0,     0,  ...,    67, 12904,    49]])
Sample input:
tensor([0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1,
0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0,
1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0,
0, 0, 1, 0])


Now, we will define the SentimentRNN class. Most of the model’s class will be familiar to you, but there are two important layers we would like you to pay attention to:

• Embedding Layer

This layer is like a linear layer, but it makes it posible to use a sequence of inedexes as inputs (instead of a sequence of one-hot-encoded vectors). During training, the Embedding layer learns a linear transformation from the space of words (a vector space of dimension num_words_dict) into the a new, smaller, vector space of dimension embedding_dim. We suggest you to read this thread and the pytorch documentation if you want to learn more about this particular kind of layers.

• LSTM layer

This is one of the most used class of Recurrent Neural Networks. In Pytorch we can add several stacked layers in just one line of code. In our case, the number of layers added are decided with the parameter no_layers. If you want to learn more about LSTMs we strongly recommend you this Colahs thread about them.

class SentimentRNN(nn.Module):
def __init__(self,no_layers,vocab_size,hidden_dim,embedding_dim,drop_prob=0.1):
super(SentimentRNN,self).__init__()

self.output_dim = output_dim
self.hidden_dim = hidden_dim
self.no_layers = no_layers
self.vocab_size = vocab_size
self.drop_prob = drop_prob

# Embedding Layer
self.embedding = nn.Embedding(vocab_size, embedding_dim)

# LSTM Layers
self.lstm = nn.LSTM(input_size=embedding_dim,hidden_size=self.hidden_dim,
num_layers=no_layers, batch_first=True,
dropout=self.drop_prob)

# Dropout layer
self.dropout = nn.Dropout(drop_prob)

# Linear and Sigmoid layer
self.fc = nn.Linear(self.hidden_dim, output_dim)
self.sig = nn.Sigmoid()

def forward(self,x,hidden):
batch_size = x.size(0)

# Embedding out
embeds = self.embedding(x)
#Shape: [batch_size x max_length x embedding_dim]

# LSTM out
lstm_out, hidden = self.lstm(embeds, hidden)
# Shape: [batch_size x max_length x hidden_dim]

# Select the activation of the last Hidden Layer
lstm_out = lstm_out[:,-1,:].contiguous()
# Shape: [batch_size x hidden_dim]

## You can instead average the activations across all the times
# lstm_out = torch.mean(lstm_out, 1).contiguous()

# Dropout and Fully connected layer
out = self.dropout(lstm_out)
out = self.fc(out)

# Sigmoid function
sig_out = self.sig(out)

# return last sigmoid output and hidden state
return sig_out, hidden

def init_hidden(self, batch_size):
''' Initializes hidden state '''
# Create two new tensors with sizes n_layers x batch_size x hidden_dim,
# initialized to zero, for hidden state and cell state of LSTM
h0 = torch.zeros((self.no_layers,batch_size,self.hidden_dim)).to(device)
c0 = torch.zeros((self.no_layers,batch_size,self.hidden_dim)).to(device)
hidden = (h0,c0)
return hidden


We choose the parameters of the model.

# Parameters of our network

# Size of our vocabulary
vocab_size = num_words_dict

# Embedding dimension
embedding_dim = 32

# Number of stacked LSTM layers
no_layers = 2

# Dimension of the hidden layer in LSTMs
hidden_dim = 64

# Dropout parameter for regularization
output_dim = 1

# Dropout parameter for regularization
drop_prob = 0.25

# Let's define our model
model = SentimentRNN(no_layers, vocab_size, hidden_dim,
embedding_dim, drop_prob=drop_prob)
# Moving to gpu
model.to(device)
print(model)

SentimentRNN(
(embedding): Embedding(30000, 32)
(lstm): LSTM(32, 64, num_layers=2, batch_first=True, dropout=0.25)
(dropout): Dropout(p=0.25, inplace=False)
(fc): Linear(in_features=64, out_features=1, bias=True)
(sig): Sigmoid()
)

# How many trainable parameters does our model have?
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print('Total Number of parameters: ',params)

Total Number of parameters:  1018433


We choose the losses and the optimizer for the training procces.

# loss and optimization functions
lr = 0.001

# Binary crossentropy is a good loss function for a binary classification problem
criterion = nn.BCELoss()

# We choose an Adam optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=lr)

# function to predict accuracy
def acc(pred,label):
pred = torch.round(pred.squeeze())
return torch.sum(pred == label.squeeze()).item()


We are ready to train our model.

# Number of training Epochs
epochs = 5

# Maximum absolute value accepted for the gradeint
clip = 5

# Initial Loss value (assumed big)
valid_loss_min = np.Inf

# Lists to follow the evolution of the loss and accuracy
epoch_tr_loss,epoch_vl_loss = [],[]
epoch_tr_acc,epoch_vl_acc = [],[]

# Train for a number of Epochs
for epoch in range(epochs):
train_losses = []
train_acc = 0.0
model.train()

for inputs, labels in train_loader:

# Initialize hidden state
h = model.init_hidden(batch_size)
# Creating new variables for the hidden state
h = tuple([each.data.to(device) for each in h])

# Move batch inputs and labels to gpu
inputs, labels = inputs.to(device), labels.to(device)

# Set gradient to zero
model.zero_grad()

# Compute model output
output,h = model(inputs,h)

# Calculate the loss and perform backprop
loss = criterion(output.squeeze(), labels.float())
loss.backward()
train_losses.append(loss.item())

# calculating accuracy
accuracy = acc(output,labels)
train_acc += accuracy

#clip_grad_norm helps prevent the exploding gradient problem in RNNs / LSTMs.
nn.utils.clip_grad_norm_(model.parameters(), clip)
optimizer.step()

# Evaluate on the validation set for this epoch
val_losses = []
val_acc = 0.0
model.eval()
for inputs, labels in valid_loader:

# Initialize hidden state
val_h = model.init_hidden(batch_size)
val_h = tuple([each.data.to(device) for each in val_h])

# Move batch inputs and labels to gpu
inputs, labels = inputs.to(device), labels.to(device)

# Compute model output
output, val_h = model(inputs, val_h)

# Compute Loss
val_loss = criterion(output.squeeze(), labels.float())

val_losses.append(val_loss.item())

accuracy = acc(output,labels)
val_acc += accuracy

epoch_train_loss = np.mean(train_losses)
epoch_val_loss = np.mean(val_losses)
epoch_train_acc = train_acc/len(train_loader.dataset)
epoch_val_acc = val_acc/len(valid_loader.dataset)
epoch_tr_loss.append(epoch_train_loss)
epoch_vl_loss.append(epoch_val_loss)
epoch_tr_acc.append(epoch_train_acc)
epoch_vl_acc.append(epoch_val_acc)
print(f'Epoch {epoch+1}')
print(f'train_loss : {epoch_train_loss} val_loss : {epoch_val_loss}')
print(f'train_accuracy : {epoch_train_acc*100} val_accuracy : {epoch_val_acc*100}')
if epoch_val_loss <= valid_loss_min:
print('Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...'.format(valid_loss_min,epoch_val_loss))
# torch.save(model.state_dict(), '../working/state_dict.pt')
valid_loss_min = epoch_val_loss
print(25*'==')

Epoch 1
train_loss : 0.4367361353733577 val_loss : 0.39174133955966683
train_accuracy : 79.530625 val_accuracy : 82.3628125
Validation loss decreased (inf --> 0.391741).  Saving model ...
==================================================
Epoch 2
train_loss : 0.3765802335098851 val_loss : 0.3724124691961333
train_accuracy : 83.19140625 val_accuracy : 83.42031250000001
Validation loss decreased (0.391741 --> 0.372412).  Saving model ...
==================================================
Epoch 3
train_loss : 0.35746844720793886 val_loss : 0.365050206175074
train_accuracy : 84.16882812499999 val_accuracy : 83.7440625
Validation loss decreased (0.372412 --> 0.365050).  Saving model ...
==================================================
Epoch 4
train_loss : 0.34491546426317654 val_loss : 0.36467386982403693
train_accuracy : 84.879140625 val_accuracy : 83.77
Validation loss decreased (0.365050 --> 0.364674).  Saving model ...
==================================================
Epoch 5
train_loss : 0.33429012800217606 val_loss : 0.36189084346871825
train_accuracy : 85.44296875 val_accuracy : 84.0221875
Validation loss decreased (0.364674 --> 0.361891).  Saving model ...
==================================================

fig = plt.figure(figsize = (20, 6))
plt.subplot(1, 2, 1)
plt.plot(epoch_tr_acc, label='Train Acc')
plt.plot(epoch_vl_acc, label='Validation Acc')
plt.title("Accuracy")
plt.legend()
plt.grid()

plt.subplot(1, 2, 2)
plt.plot(epoch_tr_loss, label='Train loss')
plt.plot(epoch_vl_loss, label='Validation loss')
plt.title("Loss")
plt.legend()
plt.grid()

plt.show()


# What’s Next?¶

You can use this project template as a starting point to think about your own project. There are a lot of ways to continue, here we share with you some ideas you migth find useful:

• Work on the Preproccesing. We used a very rudimentary way to tokenize tweets. But there are better ways to preprocess the data. Can you think of a suitable way to preprocess the data for this particular task? How does the performance of the model change when the data is processed correctly?

• Work on the Model. The RNN model proposed in this notebook is not optimized at all. You can work on finding a better architecture or better hyperparamenters. May be using bidirectonal LSTMs or increasing the number of stacked layers can improve the performance, feel free to try different approaches.

• Work on the Embedding. Our model learnt an embedding during the training on this Twitter corpus for a particular task. You can explore the representation of different words in this learned embedding. Also, you can try using different word embeddings. You can train them on this corpus or you can use an embedding trained on another corpus of data. How does the change of the embedding affect the model performance?

• Try sentiment analysis on another dataset. There are lots of available dataset to work with, we can help you find one that is interesting to you. Do you belive that a sentiment analysis model trained on some corpus (Twitter dataset) will perform well on another type of data (for example, youtube comments)?