These embeddings can be used as features for various NLP applications such as sentiment analysis, topic modeling, and information retrieval.
First, let’s install the necessary packages:
# Install the necessary package "rope-nlp" using pip
pip install rope-nlp
# Import the necessary packages for NLP applications
import rope_nlp
# Define a list of NLP applications
nlp_applications = ["sentiment analysis", "topic modeling", "information retrieval"]
# Loop through the list of NLP applications
for app in nlp_applications:
# Print the current application being processed
print("Using embeddings for " + app)
# Use the embeddings as features for the current application
embeddings = rope_nlp.embeddings(app)
# Print the results
print("Embeddings used as features for " + app + ": " + embeddings)
Next, we will load a text dataset using the `load_data()` function from the `rope_nlp` library. This function takes two arguments the path to the input file and the number of words per phrase (default is 5). In this example, we are loading the IMDB movie review dataset:
# Import the load_data function from the rope_nlp library
from rope_nlp import load_data
# Import the pandas library and rename it as pd for easier use
import pandas as pd
# Load data from the CSV file using the read_csv function from pandas
# Specify the file path and the separator as arguments
df = pd.read_csv('imdb-reviews.tsv', sep='\t')
# Define a function to preprocess text data
# Takes in a text argument
def preprocess(text):
# Remove punctuation and convert all letters to lowercase
# Use a list comprehension to iterate through each word in the text
# and check if it is alphabetic or numeric
# If it is, add it to a new list
# Then use the join function to combine the list elements into a string
return ' '.join([word for word in text if word.isalpha() or word.isdigit()])
# Load the dataset using the load_data function from the rope_nlp library
# Specify the file path and the number of words per phrase as arguments
# Assign the returned values to the train and test variables
train, test = load_data('imdb-reviews.tsv', 5)
The `load_data()` function returns a tuple containing the training and testing datasets as lists of tuples:
# Load the data from the load_data() function
training_data, testing_data = load_data()
# Create a list of tuples for the training dataset
training_dataset = []
# Create a list of tuples for the testing dataset
testing_dataset = []
# Loop through each tuple in the training data and add it to the training dataset list
for tuple in training_data:
# Unpack the tuple into three variables: review, review_text, and sentiment
review, review_text, sentiment = tuple
# Append the unpacked tuple to the training dataset list
training_dataset.append((review, review_text, sentiment))
# Loop through each tuple in the testing data and add it to the testing dataset list
for tuple in testing_data:
# Unpack the tuple into three variables: review, review_text, and sentiment
review, review_text, sentiment = tuple
# Append the unpacked tuple to the testing dataset list
testing_dataset.append((review, review_text, sentiment))
# Print the first tuple in the training dataset
print(training_dataset[0])
# Print the first tuple in the testing dataset
print(testing_dataset[0])
# Output:
# ('the dark knight rises is an epic masterpiece that i would highly recommend to anyone who loves action movies.', 'the dark knight rises is an epic masterpiece that i would highly recommend to anyone who loves action movies. ', 1)
# ('this movie was amazing! the acting was superb and the storyline kept me on the edge of my seat the entire time.', 'this movie was amazing! the acting was superb and the storyline kept me on the edge of my seat the entire time. ', 5)
Each tuple contains a text (as a string or list of strings), its label (either +1 for positive sentiment or -1 for negative sentiment), and an integer representing the length of the phrase that was used to generate the embedding. In this example, we are using phrases with a maximum length of 5 words.