Generate a Sklearn model from resturant reviews, and evaluate user input against the multilabel-classification model. Code heavily borrowed from https://www.geeksforgeeks.org/an-introduction-to-multilabel-classification/

multiple-label-classification-reviews.py

"""
Produced 2020
By https://amattu.com/links/github
Copy Alec M.
"""

"""
Original Author
https://www.geeksforgeeks.org/an-introduction-to-multilabel-classification/
"""

# Imports
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from skmultilearn.adapt import MLkNN

aspects_df = pd.read_csv('semeval2014.csv')
X = aspects_df["text"]
y = np.asarray(aspects_df[aspects_df.columns[1:]])

# initializing TfidfVectorizer
vetorizar = TfidfVectorizer(max_features = 3000, max_df = 0.85)
vetorizar.fit(X)

# splitting the data to training and testing data set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state = 42)
X_train_tfidf = vetorizar.transform(X_train)
X_test_tfidf = vetorizar.transform(X_test)

# using Multi-label kNN classifier
mlknn_classifier = MLkNN()
mlknn_classifier.fit(X_train_tfidf, y_train)
review = ""

# Continue reading until KeyboardInterupt
while review == "":
    review = input("Enter a sentence: ")
    new_sentence_tfidf = vetorizar.transform([review])

    prediction = mlknn_classifier.predict(new_sentence_tfidf).toarray()[0]
    print("Service: {}\nFood: {}\nAnecdote: {}\nPrice: {}\nAmbience: {}".format(prediction[0], prediction[1], prediction[2], prediction[3], prediction[4]))
    review = ""

semeval2014.csv