How to Perform Sentiment Analysis on Amazon Product Reviews Using Random Forest Classifier in Python?
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Condition for Performing Sentiment Analysis on Amazon Product Reviews Using Random Forest Classifier in Python
Description: Analyze textual data from Amazon reviews and classify sentiments using the Random Forest Classifier.
Goal: Understand and implement sentiment analysis with a machine learning model.
Why Should We Choose Random Forest Classifier?
Versatility: Works for classification and regression.
Robust: Resists overfitting better than single decision trees.
Step by Step Process
Step 1: Data Collection
Step 2: Data Preprocessing
Step 3: Feature Extraction
Step 4: Model Building
Step 5: Model Evaluation
Step 6: Visualization
Step 7: Model Deployment (Optional)
Sample Source Code
# Importing Libraries
import pandas as pd
import numpy as np
import re
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import classification_report, confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
# Simulate a small dataset
data = {'review': [...], 'sentiment': [...]}
# Data Preprocessing
def clean_text(text):
# Remove unwanted elements and lower the case
return text
# Vectorization and Model Training
model.fit(X_train, y_train)
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