The sentiment analysis approach has become an essential customer analysis as a result of the fast growth of internet technology and social media. Several studies have detailed the efficacy of several sentiment classifications, ranging from lexicon-based to machine learning approaches. While lexicon-based approaches have limitations due to the limited number of terms in dictionaries and labeled data, machine learning approaches are frequently flawed because it requires massive datasets to train each domain dataset. This paper presents a framework and algorithms that bridge the gap between the lexicon and linked open data methods (DBpedia) for resolving semantic conflicts and improving SentiWordNet's sentiment score to obtain higher performance. Furthermore, we also evaluate our sentiment classification using precision, recall, and F-measure metrics, which have values of 0.76, 0.91, and 0.82, respectively.

Aspect-Based Sentiment Analysis (ABSA) is a crucial process for assessing customer feedback and gauging satisfaction with products or services. It typically consists of three stages: Aspect Term Extraction (ATE), Aspect Categorization Extraction (ACE), and Sentiment Analysis (SA). Various techniques have been proposed for ATE, including unsupervised, supervised, and hybrid methods. However, many studies face challenges in detecting aspect terms due to reliance on training data, which may not cover all multiple aspect terms and relate semantic aspect terms effectively. This study presents a knowledge-driven approach to automatic semantic aspect term extraction from customer feedback using Linked Open Data (LOD) to enrich aspect extraction outcomes in the training dataset. Additionally, it utilizes the N-gram model to capture complex text patterns and relationships, facilitating accurate classification and analysis of multiple-word terms for each aspect. To assess the effectiveness of the proposed model, experiments were conducted on three benchmark datasets: SemEval 2014, 2015, and 2016. Comparative evaluations with contemporary unsupervised, supervised, and hybrid methods on these datasets yielded F-measures of 0.80, 0.76, and 0.77, respectively.