How to Perform Sentiment Analysis on Amazon Product Reviews Using Support Vector Machine in Python?
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Condition for Performing Sentiment Analysis on Amazon Product Reviews Using Support Vector Machine in Python
Description: Sentiment analysis involves determining the sentiment or opinion expressed in a text. In the case of Amazon product reviews, this means identifying whether the review is positive, negative, or neutral. This project aims to perform sentiment analysis on Amazon product reviews using the Support Vector Machine (SVM) algorithm, a supervised learning method that is effective for classification tasks.
Goal: Process textual data (Amazon product reviews), extract features, and classify the reviews into different sentiment categories (positive, negative, or neutral) using SVM.
Evaluation Metrics: Evaluate the model using performance metrics such as accuracy, precision, recall, and F1-score.
Why Should We Choose Support Vector Machine (SVM)?
Effectiveness in high-dimensional spaces: SVM works well in high-dimensional spaces, ideal for text classification tasks.
Good generalization performance: SVM generalizes well to unseen data, especially in binary classification tasks like sentiment analysis.
Step by Step Process
Data Collection: Use Amazon product review data. Scrape reviews using BeautifulSoup or use pre-collected datasets from Kaggle.
Preprocessing: Clean the text (remove stop words, perform stemming/lemmatization) and tokenize.
Feature Extraction: Use TF-IDF to convert text into numerical vectors for SVM training.
Data Splitting: Split the dataset into training and testing sets (e.g., 80% training, 20% testing).
Model Training: Train the SVM classifier using the training data with the selected kernel (linear or RBF).
Model Evaluation: Evaluate using accuracy, precision, recall, F1-score, and confusion matrix.
Visualization: Plot training curves and word clouds for positive/negative reviews.
Model Tuning: Optimize using GridSearchCV for hyperparameter tuning.
Deployment: Deploy the model using Flask or FastAPI for real-time sentiment classification (optional).
Sample Source Code
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
from sklearn.metrics import classification_report, confusion_matrix
from wordcloud import WordCloud
import seaborn as sns
import re
from nltk.stem import PorterStemmer
from nltk.corpus import stopwords
import nltk
# Download NLTK stopwords if not already installed
nltk.download('stopwords')
# Creating a simple mock dataset
data = {
'review': [
"I love this product! It's amazing.",
"Worst purchase I've ever made.",
"Decent quality, would recommend.",
"Not as expected, very disappointing.",
"Fantastic, will buy again!",
"Excellent value for money, highly recommend.",
"Terrible, broke after one use.",
"So good, exceeded my expectations.",
"Would not buy again, very poor quality.",
"Just okay, nothing special."
],
'sentiment': ['positive', 'negative', 'positive', 'negative', 'positive',
'positive', 'negative', 'positive', 'negative', 'neutral']
}
# Create DataFrame
df = pd.DataFrame(data)
# Step 2: Preprocessing
def clean_text(text):
text = text.lower()
text = re.sub(r'[^a-z\s]', '', text)
return text
# Apply text cleaning
df['cleaned_review'] = df['review'].apply(clean_text)
# Step 3: Feature Extraction using TF-IDF
vectorizer = TfidfVectorizer(stop_words='english', max_features=1000)
X = vectorizer.fit_transform(df['cleaned_review'])
y = df['sentiment']
# Step 4: Split Data into Training and Testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)