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How to Perform Sentiment Analysis on Amazon Product Reviews Using Decision Tree Algorithm in Python?

Sentiment Analysis using Decision Tree

Condition for Performing Sentiment Analysis on Amazon Product Reviews Using Decision Tree Algorithm in Python

  • Description: Sentiment analysis is a Natural Language Processing (NLP) task that involves determining the sentiment expressed in a text, such as product reviews. In this project, we will analyze Amazon product reviews to determine whether a review is positive, neutral, or negative using a Decision Tree algorithm. Decision Trees are a popular machine learning algorithm known for their simplicity and interpretability.
Why Should We Choose Decision Tree Algorithm?
  • Interpretability: Decision Trees are highly interpretable compared to other machine learning algorithms. The decision-making process can be easily visualized, making it a great tool for understanding how decisions are made.
  • Non-Linear Relationships: Decision Trees can capture non-linear relationships in the data, which is useful when dealing with complex textual data like reviews.
  • Less Data Preprocessing: Decision Trees require minimal data preprocessing. They can handle both numerical and categorical data and do not require feature scaling.
  • Handles Missing Data: Decision Trees can handle missing data, which is often the case in real-world datasets like product reviews.
Step-by-Step Process
  • Data Collection: Use the "Amazon Product Review" dataset. For this project, we will focus on reviews from a variety of product categories available on Amazon. You can download the dataset from sources like Kaggle or Amazon itself.
  • Data Preprocessing: Load the dataset into a Pandas DataFrame. Clean the data by removing missing values, duplicate entries, and irrelevant columns. Perform text preprocessing like tokenization, removing stopwords, and stemming or lemmatization.
  • Feature Extraction: Convert the text data into numerical features using techniques like TF-IDF (Term Frequency-Inverse Document Frequency) or Bag of Words.
  • Model Building: Split the dataset into training and testing sets (typically 80/20). Train a Decision Tree classifier on the training data.
  • Model Evaluation: Evaluate the model's performance using accuracy, precision, recall, and F1-score. Optionally, visualize the decision tree and analyze the results.
  • Visualization: Generate plots to visualize the performance of the model (e.g., confusion matrix, classification report).
Sample Source Code
  • import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    import re
    import seaborn as sns
    from sklearn.metrics import confusion_matrix
    import string
    import nltk
    from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
    from sklearn.model_selection import train_test_split
    from sklearn.tree import DecisionTreeClassifier
    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 Decision Tree classification
    # Evaluate accuracy and confusion matrix

Screenshots
  • Sentiment Analysis Screenshot