<p>Online reviews are frequently utilized to determine a product's quality before purchase along with the photographs and one-to-five star ratings. The research addressed the two distinct problems observed in the review systems. </p> <p>First, due to thousands of reviews for a product, the different characteristics of customer evaluations, such as consumer sentiments, cannot be understood by manually reading only a few reviews. Second, from these reviews, it is extremely hard to understand the change in these sentiments and other important product aspects over the years (temporal analysis). To address these problems, the study focused on 2 main research parts.</p> <p>Part one of the research was focused on answering how topic modelling and sentiment analysis can work together to give deeper understanding on attribute-based product review. The second part compared different topic modelling approaches to evaluate the performances and advantages of emerging NLP models. For this purpose, a dataset consisting of 469 publicly accessible Amazon evaluations of the Kindle E-reader and 15,000 reviews of iPhone products was utilized to examine sentiment Analysis and Topic modelling. Latent Dirichlet Allocation topic model and BERTopic topic model were used to perform topic modelling and to acquire the diverse topics of concern. Sentiment Analysis was carried out to better understand each topic's positive and negative tones. Topic analysis of Kindle user evaluations revealed the following major themes: (a) leisure consumption, (b) utility as a gift, (c) pricing, (d) parental control, (e) reliability and durability, and (f) charging. While the main themes emerged from the analysis of iPhone reviews depended on the model and year of the device, some themes were found to be consistent across all the iPhone models including (a) Apple vs Android (b) utility as gift and (c) service. The study's approach helped to analyze customer reviews for any product, and the study results provided a deeper understanding of the product's strengths and weaknesses based on a comprehensive analysis of user feedback useful for product makers, retailers, e-commerce platforms, and consumers.</p>

abstract: Comparative life cycle assessment (LCA) evaluates the relative performance of multiple products, services, or technologies with the purpose of selecting the least impactful alternative. Nevertheless, characterized results are seldom conclusive. When one alternative performs best in some aspects, it may also performs worse in others. These tradeoffs among different impact categories make it difficult to identify environmentally preferable alternatives. To help reconcile this dilemma, LCA analysts have the option to apply normalization and weighting to generate comparisons based upon a single score. However, these approaches can be misleading because they suffer from problems of reference dataset incompletion, linear and fully compensatory aggregation, masking of salient tradeoffs, weight insensitivity and difficulties incorporating uncertainty in performance assessment and weights. Consequently, most LCA studies truncate impacts assessment at characterization, which leaves decision-makers to confront highly uncertain multi-criteria problems without the aid of analytic guideposts. This study introduces Stochastic Multi attribute Analysis (SMAA), a novel approach to normalization and weighting of characterized life-cycle inventory data for use in comparative Life Cycle Assessment (LCA). The proposed method avoids the bias introduced by external normalization references, and is capable of exploring high uncertainty in both the input parameters and weights. / Dissertation/Thesis / Doctoral Dissertation Civil and Environmental Engineering 2015