Sentiment Enhanced Food Search Engine

Abstract

"Sentiment analysis, a specialized field within NLP, plays a central role in this research. It involves the extraction and interpretation of sentiments from textual customer reviews, ranging from enthusiastic praise to constructive criticism and covering aspects such as food quality, service, ambiance, and overall customer satisfaction. By assigning sentiment scores to various food establishments based on the analysis of these reviews, the system quantifies the collective sentiments of customers. Within the theoretical landscape of food search applications, a notable gap exists in the integration of sentiment analysis and machine learning techniques to develop a comprehensive search engine that leverages customer sentiments for enhanced decision-making. While current platforms predominantly offer proximity-based outcomes, the theoretical gap resides in the unexplored application of sentiment analysis models to aggregate and evaluate customer reviews systematically (Moschitti, 2016). This untapped theoretical territory underscores the potential for a paradigm shift in culinary decision-making that transcends geographical constraints (Xing Fang, 2015). Subsequently, the data were trained using different machine learning (ML) models such as Multinomial Naïve Bayes (MNB), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT). During the training phase, word embedding techniques such as Term Frequency-Inverse Document Frequency (TF-IDF) were employed, and pre-processing techniques like stemming were applied. Among various techniques, SVM achieved an accuracy score of 88%. This implies that 88% of the entire search results aligned with the expected output based on sentiment reviews."