A Multi-Model Approach to Aspect-Based Sentiment Analysis of Car Reviews

What is it about?

This study focuses on analyzing sentiments in car reviews from the CarDekho website, specifically targeting ten different aspects of the cars, such as performance, build quality, and price. The reviews cover four car models from four popular brands. We used five different models—Logistic Regression, Naive Bayes, Support Vector Machine (SVM), a simple neural network (NN), and the Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM)—to determine the sentiment of the reviews for each aspect. Each model was trained and tested separately, and their performance was evaluated based on how accurately they classified the sentiments. The results offer insights into how well each model performs in this task, which can help consumers make better purchasing decisions and give manufacturers a detailed understanding of customer feedback.

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Why is it important?

This research is important because it enhances our understanding of how to analyze and interpret customer feedback in the automobile industry using advanced machine learning and deep learning techniques. By focusing on specific aspects of car reviews such as design, engine, and mileage, the study provides detailed insights into customer sentiments, which can help consumers make more informed purchasing decisions. Additionally, it aids manufacturers in gaining a deeper understanding of customer preferences and concerns, enabling them to improve their products and services. The research also contributes to the broader field of Aspect-Based Sentiment Analysis (ABSA), demonstrating the strengths and limitations of various models, and highlighting the potential of these methods to accurately predict sentiments in diverse contexts. This knowledge is crucial as the reach of the internet continues to expand, pushing the boundaries of sentiment analysis applications. A unique aspect of this work is its comprehensive comparative analysis of multiple models, including both traditional machine learning methods like Logistic Regression, Naive Bayes, and Support Vector Machines (SVM), as well as deep learning techniques such as Neural Networks and Recurrent Neural Networks with Long Short-Term Memory (RNN-LSTM). This approach provides a detailed evaluation of each model's performance across different aspects of car reviews, offering valuable insights into their respective strengths and limitations. The standout performance of the SVM and Logistic Regression models, particularly in predicting sentiments related to Build Quality, Space, and Price, underscores their robustness and versatility, contributing practical knowledge to the ABSA field.

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