The following example shows the implementation of sentiment analysis using an RNN. It has fixed-length movie reviews encoded as integer values, which are then converted to word embedding (embedding vectors) passed to LSTM layers in a recurrent manner that pick the last prediction as the output sentiment:
import numpy as npimport tensorflow as tffrom string import punctuationfrom collections import Counter'''movie review dataset for sentiment analysis'''with open('data/reviews.txt', 'r') as f: movieReviews = f.read()with open('data/labels.txt', 'r') as f: labels = f.read()'''data cleansing - remove punctuations'''text = ''.join([c for c in movieReviews if c not in punctuation])movieReviews = text.split('\n'
