# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import nltk
import matplotlib.pyplot as plt

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
train=pd.read_csv('/kaggle/input/twitter-sentiment-analysis-hatred-speech/train.csv')
test=pd.read_csv('/kaggle/input/twitter-sentiment-analysis-hatred-speech/test.csv')
train.head()
labels=train['label']
train=train.drop('label',axis=1)
labels
from nltk.sentiment.vader import SentimentIntensityAnalyzer
"""
# USING SENTIMENT INTENSITY ANALYZER FROM NLTK
 SentimentIntensityAnalyzer is a pretrained model which will be used to analyze the sentiment of a sentence.
"""
model=SentimentIntensityAnalyzer()
sentiment=[]
for i in range(len(test)):
    prob=model.polarity_scores(train['tweet'][i])
    if(prob['pos']>prob['neu'] and prob['pos']>prob['neg']):
        sentiment.append('Positive')
    elif(prob['neg']>prob['pos'] and prob['neg']>prob['neu']):
        sentiment.append('Negative')
    else:
        sentiment.append('Neutral')
print(sentiment)