Domain-Specific Sentiment Analysis in Textual Feedback: AI-Powered Aspect Detection and Sentiment Analysis for Textual Feedback and Reviews

In the era of digitalisation, customer feedback, and online reviews have become major sources which give insights for businesses. In-depth competitive research is one of the main business strategies that provides insights which will give the businesses a competitive edge among others and improve customer loyalty. Sentiment analysis is one such method for competitive research that draws public interest, exposes market trends and analyses competitors. It is an important process for understanding public opinions and sentiments on various subjects, products and services. While traditional sentiment analysis focuses on overall sentiment, Aspect-Based Sentiment Analysis (ABSA) focuses on identifying specific aspects or features within the text and analyzing the sentiment associated with each aspect. A clearer understanding of the opinions with detailed insights are provided by ABSA. With the current trending Artificial Intelligence (AI) techniques, a huge leap in sentiment analysis could be seen. These improvements have paved the way from simple word-level analysis to enabling the understanding of the tone and context of statements. Due to the complexity of morphologically rich languages, complex algorithms are needed to extract and analyze specific aspects within the text. Therefore, performing ABSA accurately still remains a challenge. Our model expects to provide accurate insights of the sentiments associated for different aspects or features in the text while focusing on a specific domain. We also hope to validate the effectiveness of our proposed model by comparing the traditional sentiment analysis techniques approaches with our model. This project aims to improve decision-making processes for individuals, governments, and organisations by improving the area of sentiment analysis and incorporating domain-specific insights. Ultimately, this will lead to improved services and more informed decisions.