How to do Text classification using word2vec

Question

I want to perform text classification using word2vec. I got vectors of words.

ls = []
sentences = lines.split(".")
for i in sentences:
    ls.append(i.split())
model = Word2Vec(ls, min_count=1, size = 4)
words = list(model.wv.vocab)
print(words)
vectors = []
for word in words:
    vectors.append(model[word].tolist())
data = np.array(vectors)
data

output:

array([[ 0.00933912,  0.07960335, -0.04559333,  0.10600036],
       [ 0.10576613,  0.07267512, -0.10718666, -0.00804013],
       [ 0.09459028, -0.09901826, -0.07074171, -0.12022413],
       [-0.09893986,  0.01500741, -0.04796079, -0.04447284],
       [ 0.04403428, -0.07966098, -0.06460238, -0.07369237],
       [ 0.09352681, -0.03864434, -0.01743148,  0.11251986],.....])

How can i perform classification (product & non product)?

Answer 1

You already have the array of word vectors using model.wv.syn0. If you print it, you can see an array with each corresponding vector of a word.

You can see an example here using Python3:

import pandas as pd
import os
import gensim
import nltk as nl
from sklearn.linear_model import LogisticRegression


#Reading a csv file with text data
dbFilepandas = pd.read_csv('machine learning\\Python\\dbSubset.csv').apply(lambda x: x.astype(str).str.lower())

train = []
#getting only the first 4 columns of the file 
for sentences in dbFilepandas[dbFilepandas.columns[0:4]].values:
    train.extend(sentences)
  
# Create an array of tokens using nltk
tokens = [nl.word_tokenize(sentences) for sentences in train]

Now it's time to use the vector model, in this example we will calculate the LogisticRegression.

# method 1 - using tokens in Word2Vec class itself so you don't need to train again with train method
model = gensim.models.Word2Vec(tokens, size=300, min_count=1, workers=4)

# method 2 - creating an object 'model' of Word2Vec and building vocabulary for training our model
model = gensim.models.Word2vec(size=300, min_count=1, workers=4)
# building vocabulary for training
model.build_vocab(tokens)
print("\n Training the word2vec model...\n")
# reducing the epochs will decrease the computation time
model.train(tokens, total_examples=len(tokens), epochs=4000)
# You can save your model if you want....

# The two datasets must be the same size
max_dataset_size = len(model.wv.syn0)

Y_dataset = []
# get the last number of each file. In this case is the department number
# this will be the 0 or 1, or another kind of classification. ( to use words you need to extract them differently, this way is to numbers)
with open("dbSubset.csv", "r") as f:
    for line in f:
        lastchar = line.strip()[-1]
        if lastchar.isdigit():
            result = int(lastchar) 
            Y_dataset.append(result) 
        else:
            result = 40 


clf = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial').fit(model.wv.syn0, Y_dataset[:max_dataset_size])

# Prediction of the first 15 samples of all features
predict = clf.predict(model.wv.syn0[:15, :])
# Calculating the score of the predictions
score = clf.score(model.wv.syn0, Y_dataset[:max_dataset_size])
print("\nPrediction word2vec : \n", predict)
print("Score word2vec : \n", score)

You can also calculate the similarity of words belonging to your created model dictionary:

print("\n\nSimilarity value : ",model.wv.similarity('women','men'))

You can find more functions to use here.