Wednesday, August 14, 2024

WhatsApp Chat Sentimental Analysis Using Machine Learning Project

                                         INTRODUCTION

Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.

Watch On YOUTUBE 😃👇



FULL CODE 


import re
import pandas as pd
import numpy as np
import emoji
from collections import Counter
import matplotlib.pyplot as plt
from PIL import Image
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator

# Extract Time
def date_time(s):
    pattern = '^([0-9]+)(\/)([0-9]+)(\/)([0-9]+), ([0-9]+):([0-9]+)[ ]?(AM|PM|am|pm)? -'
    result = re.match(pattern, s)
    if result:
        return True
    return False

# Find Authors or Contacts
def find_author(s):
    s = s.split(":")
    if len(s)==2:
        return True
    else:
        return False

# Finding Messages
def getDatapoint(line):
    splitline = line.split(' - ')
    dateTime = splitline[0]
    date, time = dateTime.split(", ")
    message = " ".join(splitline[1:])
    if find_author(message):
        splitmessage = message.split(": ")
        author = splitmessage[0]
        message = " ".join(splitmessage[1:])
    else:
        author= None
    return date, time, author, message



df = pd.DataFrame(data, columns=["Date", 'Time', 'Author', 'Message'])
df['Date'] = pd.to_datetime(df['Date'])

data = df.dropna()
from nltk.sentiment.vader import SentimentIntensityAnalyzer
sentiments = SentimentIntensityAnalyzer()
data["Positive"] = [sentiments.polarity_scores(i)["pos"] for i in data["Message"]]
data["Negative"] = [sentiments.polarity_scores(i)["neg"] for i in data["Message"]]
data["Neutral"] = [sentiments.polarity_scores(i)["neu"] for i in data["Message"]]
print(data.head())


x = sum(data["Positive"])
y = sum(data["Negative"])
z = sum(data["Neutral"])

def sentiment_score(a, b, c):
    if (a>b) and (a>c):
        print("Positive 😊 ")
    elif (b>a) and (b>c):
        print("Negative 😠 ")
    else:
        print("Neutral 🙂 ")
sentiment_score(x, y, z)


at

No comments:

Post a Comment

Subscribe to: Post Comments (Atom)

SQL INJECTION DETECTION USING RANDOM FOREST CLASSIFIER

  SQL INJECTION DETECTION USING RANDOM FOREST CLASSIFIER