review_class.py

#Import necessary packages
import numpy as np
import random
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.metrics import f1_score #f1 score to use it as and evaluation metric
import ast #to convert string into dictionary
from IPython.display import clear_output
from sklearn import svm #support vector machine classifier
from sklearn.metrics import confusion_matrix
from sklearn.linear_model import LogisticRegression #import logistic regression
from sklearn.tree import DecisionTreeClassifier #import Decision tree
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
import seaborn as sb


# A class to categorize a review as positive, negative or neutral
class Review:
    def __init__(self, text, score):
        self.text = text
        self.score = score
        self.sentiment = self.get_sentiment()
        
    def get_sentiment(self):
        if self.score <= 2:
            return "NEGATIVE"
        elif self.score == 3: 
            return "NEUTRAL"
        else: #Score of 4 or 5
            return "POSITIVE"