Sentiment Analysis

Sentiment Analysis of a text in the email using NLTK.

Importing the libraries

import pandas as pd
import numpy as np
nltk.download('vader_lexicon')
from nltk.sentiment.vader import SentimentIntensityAnalyzer

Loading the dataset

df = pd.read_csv("/Users/vamshi/Desktop/SMSSpamCollection.tsv",sep = "\t",header = None)
df.head()

Data preprocessing

cols = ["Response","Mail"]
df.columns = cols
df.head()
Data preprocessing

df.isnull().sum()
df["Response"].value_counts()

Finding blank or empty fields

blanks =[]
for i,lb,rv in df.itertuples():
    if type(rv) == str:
        if rv.isspace():
            blank.append(i)

def sentiment_analysis(text):
    score = senti.polarity_scores(text)
    if score["compound"] == 0:
        return "Netural"
    elif score["compound"] >0:
        return "Positive"

    else:
        return "Negative"

Adding scores to the dataframe

df['scores']= df['Mail'].apply(lambda Mail:senti.polarity_scores(Mail))
df['compound_score']= df['scores'].apply(lambda k:k['compound'])
df['sentiment'] = df['compound_score'].apply(lambda compound_score: "Positive" if compound_score >= 0 else "Negative")