Introduction
Sentiment analysis is a subfield of Natural Language Processing (NLP) that involves determining the sentiment or opinion expressed in a given text. In this programming assignment, we will perform sentiment analysis on Amazon product reviews. Specifically, we will focus on a single category of products from the Amazon Product Review Dataset provided by http://jmcauley.ucsd.edu/data/amazon/. The dataset should contain a minimum of 25,000 reviews, with ratings above 3.0 considered positive and ratings equal to or below 3.0 considered negative.
Module 2: Sentiment Analysis using Statistical NLP Techniques
Tasks:
Vector Space Models: a. CountVectorizer: This vectorizer represents text documents as a matrix of token counts. b. TF-IDF: TF-IDF stands for Term Frequency-Inverse Document Frequency. It represents text documents as a matrix of TF-IDF features. c. External Vectorizer: Choose any external vectorizer (cite the original paper) that offers alternative approaches to represent text data.
Sentiment Analysis using Classical ML Techniques: a. Naive Bayes Model: Naive Bayes is a probabilistic classifier that works well for sentiment analysis tasks. b. Decision Tree: Decision Tree algorithms provide an intuitive way to classify text based on predefined rules. c. Logistic Regression: Logistic Regression is a popular binary classification algorithm widely used in sentiment analysis tasks.
Reporting Metrics: For each combination of vector space models and classical ML techniques, report the following metrics:
Accuracy: Measures the overall performance of the classifier.
F1 Score: Represents the harmonic mean of precision and recall, providing a balanced evaluation of the classifier's performance.
Confusion Matrix: Displays the number of true positive, true negative, false positive, and false negative predictions.
Analysis of Results: Analyze and compare the results obtained from different combinations of vector space models and classical ML techniques. Clearly report which vector space model is providing better results for each ML technique used.
In this programming assignment, we explored sentiment analysis on Amazon product reviews using statistical NLP techniques. We employed various vector space models, including CountVectorizer, TF-IDF, and an external vectorizer, to represent the text data. Additionally, we utilized classical ML techniques such as Naive Bayes, Decision Tree, and Logistic Regression for sentiment classification. By reporting accuracy, F1 score, and confusion matrix for each combination, we obtained a comprehensive evaluation of the models' performance. Through the analysis of results, we can identify the vector space model that consistently yields better results for each ML technique. This assignment provides a practical understanding of sentiment analysis and the application of NLP techniques to extract valuable insights from textual data.
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