text_plain_output_1.png
from nltk.sentiment.vader import SentimentIntensityAnalyzer

from sklearn.utils import shuffle

import pandas as pd

data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)

data_df = pd.DataFrame(data)

data_df = shuffle(data_df)

data_df = data_df.dropna()

corr_matrix = data_df.corr()
corr_matrix['Rating'].sort_values(ascending=False)

corr_matrix = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)

all_reviews = data_df['Reviews']

data_df = data_df.reset_index(drop=True)

all_reviews = data_df['Reviews']
all_sent_values = []
all_sentiments = []

from nltk.sentiment.vader import SentimentIntensityAnalyzer
def sentiment_value(paragraph):
    analyser = SentimentIntensityAnalyzer()
    result = analyser.polarity_scores(paragraph)
    score = result['compound']
    return round(score, 1)

for i in range(0, 20000):
    all_sent_values.append(sentiment_value(all_reviews[i]))

len(all_sent_values)