Publication Date

Spring 2024

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Engineering

Advisor

Mahima Agumbe Suresh; Wencen Wu; Vimal Viswanathan

Abstract

As technology advances, computers become increasingly proficient at interpreting and translating human language into machine-understandable text. With the help of algorithms in natural language processing (NLP), machines can now translate textual data. These algorithms help identify and extract specific text components known as aspects. The aspects represent specific attributes or topics within textual data. For instance, an Amazon review states,“ This phone has good battery life but poor camera quality,” and attributes like ‘battery life’ and ‘camera quality’ represent aspects in the text. Aspect extraction is a pivotal process involving identifying and isolating key features or topics within text. This research aims to compare and discuss the existing aspect extraction techniques. By effectively extracting the aspects, we will help machines gain the capability to understand and analyze sentiments, thereby enhancing their ability to derive meaningful insights from diverse textual data. Aspect-based sentiment analysis (ABSA) enables the extraction of sentiments towards specific aspects of a product the user provides. For example, when a person writes a review about a restaurant, sentiment analysis can determine whether the review is positive or negative. Sentiment analysis helps us determine the polarity of that review. ABSA can separately determine the review’s sentiment towards different aspects of a restaurant, such as, food quality, ambiance, etc. We propose an approach to extract features/aspects of customer-based product reviews. Our approach is divided into two parts: first, the sentiment analysis of the reviews, which tells whether the review is positive or negative, and second, extracting the aspects from the reviews. The system then converts the consumer experience into text to help both new and seasoned merchants to improve their products. The suggested outcome will result in better consumer experience and business growth.

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Available for download on Monday, February 16, 2026

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