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Recurrent Neural Networks
Example ‒ Many-to-One – Sentiment Analysis
In this example, we will use a many-to-one network that takes a sentence as input
and predicts its sentiment as being either positive or negative. Our dataset is the
Sentiment labeled sentences dataset on the UCI Machine Learning Repository [20],
a set of 3,000 sentences from reviews on Amazon, IMDb, and Yelp, each labeled
with 0 if it expresses a negative sentiment, or 1 if it expresses a positive sentiment.
As usual, we will start with our imports:
import numpy as np
import os
import shutil
import tensorflow as tf
from sklearn.metrics import accuracy_score, confusion_matrix
The dataset is provided as a zip file, which expands into a folder containing three
files of labeled sentences, one for each provider, with one sentence and label per
line, with the sentence and label separated by the tab character. We first download
the zip file, then parse the files into a list of (sentence, label) pairs:
def download_and_read(url):
local_file = url.split('/')[-1]
local_file = local_file.replace("%20", " ")
p = tf.keras.utils.get_file(local_file, url,
extract=True, cache_dir=".")
local_folder = os.path.join("datasets", local_file.split('.')[0])
labeled_sentences = []
for labeled_filename in os.listdir(local_folder):
if labeled_filename.endswith("_labelled.txt"):
with open(os.path.join(
local_folder, labeled_filename), "r") as f:
for line in f:
sentence, label = line.strip().split('\t')
labeled_sentences.append((sentence, label))
return labeled_sentences
labeled_sentences = download_and_read(
"https://archive.ics.uci.edu/ml/machine-learning-databases/" +
"00331/sentiment%20labelled%20sentences.zip")
sentences = [s for (s, l) in labeled_sentences]
labels = [int(l) for (s, l) in labeled_sentences]
[ 300 ]