How to Perform Sentiment Analysis on Amazon Product Reviews Using KNN Algorithm in Python?
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Condition for Performing Sentiment Analysis on Amazon Product Reviews Using KNN Algorithm in Python
Description: Sentiment analysis is a technique used to determine whether the sentiment behind a piece of text is positive, negative, or neutral. In this project, we apply the k-Nearest Neighbors (KNN) algorithm to analyze customer reviews of products available on Amazon. By analyzing these reviews, we can determine the sentiment of customers toward products and gain insights that businesses can use to improve customer satisfaction and make informed decisions. The KNN algorithm helps classify reviews as positive or negative.
Why Should We Choose KNN?
Easy to Understand and Implement: KNN does not require complex model training. The model simply stores the entire training dataset and uses it to classify new data based on the nearest neighbors.
Non-linear Decision Boundaries: KNN can classify data with complex decision boundaries, making it effective for text classification problems.
No Training Phase: KNN is a lazy learner, meaning there is no explicit training phase. This makes it faster to implement, especially when working with small to medium-sized datasets.
Works Well with High-dimensional Data: Text data, especially after vectorization, tends to have a high number of dimensions (features). KNN can handle such high-dimensional data without requiring explicit feature selection or dimensionality reduction.
Step-by-Step Process
Data Collection: The first step involves collecting Amazon product reviews. The dataset typically contains reviews, ratings, product IDs, and other metadata.
Data Preprocessing: Text cleaning and tokenization are done to prepare the text data for analysis. Techniques like TF-IDF or Count Vectorization are used to convert the text into numerical vectors.
Feature Engineering: Features are extracted using vectorization, and sentiment labels are assigned based on ratings (positive for ratings ≥ 4, negative for ratings ≤ 2).
Model Building: The dataset is split into training and testing sets. KNN is applied to classify the reviews as positive or negative based on the labeled data.
Evaluation: The accuracy of the KNN model is evaluated using metrics like accuracy score and confusion matrix.
Visualization: Visualization tools like confusion matrix heatmaps and accuracy plots help in understanding the model's performance.
Sample Source Code
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import re
import seaborn as sns
import string
import nltk
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report, accuracy_score
# Load the dataset
temp = pd.read_csv('/path/to/your/dataset.csv')
temp.head()
# Text preprocessing and feature extraction
# Create binary label, clean text, remove stopwords, etc.
# Train-test split and KNN classification
# Evaluate accuracy and confusion matrix
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