# Sentiment Classification with Natural Language Processing on LSTM

Source: Deep Learning on Medium

So Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text. LSA is an information retrieval technique which analyzes and identifies the pattern in unstructured collection of text and the relationship between them.

LSA itself is an unsupervised way of uncovering synonyms in a collection of documents.To start, we take a look how Latent Semantic Analysis is used in Natural Language Processing to analyze relationships between a set of documents and the terms that they contain. Then we go steps further to analyze and classify sentiment. We will review Chi Squared for feature selection along the way. We will use Recurrent Neural Networks, and in particular LSTMs, to perform sentiment analysis in Keras. Let’s get started!

import pandas as pd

### Text Preprocessing

Since, text is the most unstructured form of all the available data, various types of noise are present in it and the data is not readily analyzable without any pre-processing. The entire process of cleaning and standardization of text, making it noise-free and ready for analysis is known as text preprocessing.

It is predominantly comprised of following steps:

• Noise Removal

Any piece of text which is not relevant to the context of the data and the end-output can be specified as the noise.

For example — language stopwords (commonly used words of a language — is, am, the, of, in etc), URLs or links, social media entities (mentions, hashtags), punctuations and industry specific words. This step deals with removal of all types of noisy entities present in the text.

A general approach for noise removal is to prepare a dictionary of noisy entities, and iterate the text object by tokens (or by words), eliminating those tokens which are present in the noise dictionary.

• Lexicon Normalization

Another type of textual noise is about the multiple representations exhibited by single word.

For example — “play”, “player”, “played”, “plays” and “playing” are the different variations of the word — “play”, Though they mean different but contextually all are similar. The step converts all the disparities of a word into their normalized form (also known as lemma). Normalization is a pivotal step for feature engineering with text as it converts the high dimensional features (N different features) to the low dimensional space (1 feature), which is an ideal ask for any ML model.

The most common lexicon normalization practices are :

• Stemming: Stemming is a rudimentary rule-based process of stripping the suffixes (“ing”, “ly”, “es”, “s” etc) from a word.
• Lemmatization: Lemmatization, on the other hand, is an organized & step by step procedure of obtaining the root form of the word, it makes use of vocabulary (dictionary importance of words) and morphological analysis (word structure and grammar relations).
• Stop word removal

Stop word removal is an important preprocessing step for some NLP applications, such as sentiment analysis, text summarization, and so on.

Removing stop words, as well as removing commonly occurring words, is a basic but important step. The following is a list of stop words which are going to be removed. This list has been generated from nltk.

# Cleaning the texts
import re
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
corpus = []
for i in range(0, 10000):
review = re.sub('[^a-zA-Z]', ' ', df['Text'][i])
review = review.lower()
review = review.split()
ps = PorterStemmer()
review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
review = ' '.join(review)
corpus.append(review)

### Statistical Features

Text data can also be quantified directly into numbers using several techniques described in this section:

A. Term Frequency — Inverse Document Frequency (TF — IDF)

TF-IDF is a weighted model commonly used for information retrieval problems. It aims to convert the text documents into vector models on the basis of occurrence of words in the documents without taking considering the exact ordering. For Example — let say there is a dataset of N text documents, In any document “D”, TF and IDF will be defined as –

Term Frequency (TF) — TF for a term “t” is defined as the count of a term “t” in a document “D”

Inverse Document Frequency (IDF) — IDF for a term is defined as logarithm of ratio of total documents available in the corpus and number of documents containing the term T.

TF . IDF — TF IDF formula gives the relative importance of a term in a corpus (list of documents), given by the following formula below. Following is the code using python’s scikit learn package to convert a text into tf idf vectors:Put simply, the higher the TFIDF score (weight), the rarer the word and vice versa.

from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer()
tfidf.fit(result['Reviews'])

Among the three words, “peanut”, “jumbo” and “error”, tf-idf gives the highest weight to “jumbo”. Why? This indicates that “jumbo” is a much rarer word than “peanut” and “error”. This is how to use the tf-idf to indicate the importance of words or terms inside a collection of documents.

### Sentiment Classification

To classify sentiment, we remove neutral score 3, then group score 4 and 5 to positive (1), and score 1 and 2 to negative (0). After simple cleaning up, this is the data we are going to work with.

result.dropna(inplace=True)
result[result['Score'] != 3]
result['Positivity'] = np.where(result['Score'] > 3, 1, 0)
cols = [ 'Score']
result.drop(cols, axis=1, inplace=True)

### Train Test Split

from sklearn.model_selection import train_test_split
X = df.Text
y = df.Positivity
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0)
print("Train set has total {0} entries with {1:.2f}% negative, {2:.2f}% positive".format(len(X_train),
(len(X_train[y_train == 0]) / (len(X_train)*1.))*100,
(len(X_train[y_train == 1]) / (len(X_train)*1.))*100))
print("Test set has total {0} entries with {1:.2f}% negative, {2:.2f}% positive".format(len(X_test),
(len(X_test[y_test == 0]) / (len(X_test)*1.))*100,
(len(X_test[y_test == 1]) / (len(X_test)*1.))*100))

You may have noticed that our classes are imbalanced, and the ratio of negative to positive instances is 22:78.

One of the tactics of combating imbalanced classes is using Decision Tree algorithms, so, we are using Random Forest classifier to learn imbalanced data and set class_weight=balanced .First, define a function to print out the accuracy score.

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
def accuracy_summary(pipeline, X_train, y_train, X_test, y_test):
sentiment_fit = pipeline.fit(X_train, y_train)
y_pred = sentiment_fit.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("accuracy score: {0:.2f}%".format(accuracy*100))
return accuracy

To have efficient sentiment analysis or solving any NLP problem, we need a lot of features. Its not easy to figure out the exact number of features are needed. So we are going to try, 10,000 to 30,000. And print out accuracy scores associate with the number of features.

cv = CountVectorizer()
rf = RandomForestClassifier(class_weight="balanced")
n_features = np.arange(10000,25001,5000)
def nfeature_accuracy_checker(vectorizer=cv, n_features=n_features, stop_words=None, ngram_range=(1, 1), classifier=rf):
result = []
print(classifier)
print("\n")
for n in n_features:
vectorizer.set_params(stop_words=stop_words, max_features=n, ngram_range=ngram_range)
checker_pipeline = Pipeline([
('vectorizer', vectorizer),
('classifier', classifier)
])
print("Test result for {} features".format(n))
nfeature_accuracy = accuracy_summary(checker_pipeline, X_train, y_train, X_test, y_test)
result.append((n,nfeature_accuracy))
return result
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer()
print("Result for trigram with stop words (Tfidf)\n")
feature_result_tgt = nfeature_accuracy_checker(vectorizer=tfidf,ngram_range=(1, 3))

Before we are done here, we should check the classification report.

from sklearn.metrics import classification_report
cv = CountVectorizer(max_features=30000,ngram_range=(1, 3))
pipeline = Pipeline([
('vectorizer', cv),
('classifier', rf)
])
sentiment_fit = pipeline.fit(X_train, y_train)
y_pred = sentiment_fit.predict(X_test)
print(classification_report(y_test, y_pred, target_names=['negative','positive']))

### Chi-Squared for Feature Selection

Feature selection is an important problem in Machine learning. I will show you how straightforward it is to conduct Chi square test based feature selection on our large scale data set.

We will calculate the Chi square scores for all the features and visualize the top 20, here terms or words or N-grams are features, and positive and negative are two classes. given a feature X, we can use Chi square test to evaluate its importance to distinguish the class.

from sklearn.feature_selection import chi2
tfidf = TfidfVectorizer(max_features=30000,ngram_range=(1, 3))
X_tfidf = tfidf.fit_transform(result.Reviews)
y = result.Positivity
chi2score = chi2(X_tfidf, y)[0]
plt.figure(figsize=(16,8))
scores = list(zip(tfidf.get_feature_names(), chi2score))
chi2 = sorted(scores, key=lambda x:x[1])
topchi2 = list(zip(*chi2[-20:]))
x = range(len(topchi2[1]))
labels = topchi2[0]
plt.barh(x,topchi2[1], align='center', alpha=0.5)
plt.plot(topchi2[1], x, '-o', markersize=5, alpha=0.8)
plt.yticks(x, labels)
plt.xlabel('$\chi^2$')
plt.show();

### LSTM Framework

from sklearn.feature_extraction.text import CountVectorizer
from keras.preprocessing.text import Tokenizer
from keras.models import Sequential
from keras.layers import Dense, Embedding, LSTM
from sklearn.model_selection import train_test_split
from keras.utils.np_utils import to_categorical
import re

In order to feed this data into our RNN, all input documents must have the same length. We will limit the maximum review length to max_words by truncating longer reviews and padding shorter reviews with a null value (0). We can accomplish this using the pad_sequences() function in Keras. For now, set max_words Then, I define the number of max features as 30000 and use Tokenizer to vectorize and convert text into Sequences so the Network can deal with it as input.

max_fatures = 30000
tokenizer = Tokenizer(nb_words=max_fatures, split=' ')
tokenizer.fit_on_texts(result['Reviews'].values)
X1 = tokenizer.texts_to_sequences(result['Reviews'].values)
Y1 = pd.get_dummies(result['Positivity']).values
X1_train, X1_test, Y1_train, Y1_test = train_test_split(X1,Y1, random_state = 42)
print(X1_train.shape,Y1_train.shape)
print(X1_test.shape,Y1_test.shape)

### Design an RNN model for sentiment analysis

We start building our model architecture in the code cell below. We have imported some layers from Keras that you might need but feel free to use any other layers / transformations you like.

Remember that our input is a sequence of words (technically, integer word IDs) of maximum length = max_words, and our output is a binary sentiment label (0 or 1).

### Keras Embedding Layer

Keras offers an Embedding layer that can be used for neural networks on text data.

It requires that the input data be integer encoded, so that each word is represented by a unique integer. This data preparation step can be performed using the Tokenizer API also provided with Keras.

The Embedding layer is initialized with random weights and will learn an embedding for all of the words in the training dataset.

It is a flexible layer that can be used in a variety of ways, such as:

• It can be used alone to learn a word embedding that can be saved and used in another model later.
• It can be used as part of a deep learning model where the embedding is learned along with the model itself.
• It can be used to load a pre-trained word embedding model, a type of transfer learning.

The Embedding layer is defined as the first hidden layer of a network. It must specify 3 arguments:

It must specify 3 arguments:

• input_dim: This is the size of the vocabulary in the text data. For example, if your data is integer encoded to values between 0–10, then the size of the vocabulary would be 11 words.
• output_dim: This is the size of the vector space in which words will be embedded. It defines the size of the output vectors from this layer for each word. For example, it could be 32 or 100 or even larger. Test different values for your problem.
• input_length: This is the length of input sequences, as you would define for any input layer of a Keras model. For example, if all of your input documents are comprised of 1000 words, this would be 1000.

For example, below we define an Embedding layer with a vocabulary of 200 (e.g. integer encoded words from 0 to 199, inclusive), a vector space of 32 dimensions in which words will be embedded, and input documents that have 50 words each.

e = Embedding(200, 32, input_length=50)

The Embedding layer has weights that are learned. If you save your model to file, this will include weights for the Embedding layer.The output of the Embedding layer is a 2D vector with one embedding for each word in the input sequence of words (input document).

If you wish to connect a Dense layer directly to an Embedding layer, you must first flatten the 2D output matrix to a 1D vector using the Flatten layer.

embed_dim = 150
lstm_out = 200
model = Sequential()
model.compile(loss = 'categorical_crossentropy', optimizer='adam',metrics = ['accuracy'])
print(model.summary())

To summarize, our model is a simple RNN model with 1 embedding, 1 LSTM and 1 dense layers. 4,781,202 parameters in total need to be trained.

### Train and evaluate our model

We first need to compile our model by specifying the loss function and optimizer we want to use while training, as well as any evaluation metrics we’d like to measure. Specify the appropriate parameters, including at least one metric ‘accuracy’.

batch_size = 32
model.fit(X1_train, Y1_train, nb_epoch = 3, batch_size=batch_size, verbose = 2)

Once compiled, we can kick off the training process. There are two important training parameters that we have to specify — batch size and number of training epochs, which together with our model architecture determine the total training time.

score,acc = model.evaluate(X1_test, Y1_test, verbose = 2, batch_size = batch_size)
print("score: %.2f" % (score))
print("acc: %.2f" % (acc))
score: 0.51
acc: 0.84

Finally measuring the number of correct guesses. It is clear that finding negative tweets goes very well for the Network but deciding whether is positive is not really.

pos_cnt, neg_cnt, pos_correct, neg_correct = 0, 0, 0, 0
for x in range(len(X1_test)):

result = model.predict(X1_test[x].reshape(1,X1_test.shape[1]),batch_size=1,verbose = 2)[0]

if np.argmax(result) == np.argmax(Y1_test[x]):
if np.argmax(Y1_test[x]) == 0:
neg_correct += 1
else:
pos_correct += 1

if np.argmax(Y1_test[x]) == 0:
neg_cnt += 1
else:
pos_cnt += 1

print("pos_acc", pos_correct/pos_cnt*100, "%")
print("neg_acc", neg_correct/neg_cnt*100, "%")
pos_acc 90.67439409905164 %
neg_acc 63.2890365448505 %

### Summary

There are several ways in which we can build our model. We can continue trying and improving the accuracy of our model by experimenting with different architectures, layers and parameters. How good can we get without taking prohibitively long to train? How do we prevent overfitting?

That’s it for today. Source code can be found on Github. I am happy to hear any questions or feedback. Connect with me at linkdin.