Dynamic Classification of Sentiments from Restaurant Reviews Using Novel Fuzzy-Encoded LSTM

Abstract

User reviews on social media have sparked a surge in interest in the application of sentiment analysis to provide feedback to the government, public and commercial sectors. Sentiment analysis, spam identification, sarcasm detection and news classification are just few of the uses of text mining. For many firms, classifying reviews based on user feelings is a significant and collaborative effort. In recent years, machine learning models and handcrafted features have been used to study text classification, however they have failed to produce encouraging results for short text categorization. Deep neural network based Long Short-Term Memory (LSTM) and Fuzzy logic model with incremental learning is suggested in this paper. On the basis of F1-score, accuracy, precision and recall, suggested model was tested on a large dataset of hotel reviews. This study is a categorization analysis of hotel review feelings provided by hotel customers. When word embedding is paired with LSTM, findings show that the suggested model outperforms current best-practice methods, with an accuracy 81.04%, precision 77.81%, recall 80.63% and F1-score 75.44%. The efficiency of the proposed model on any sort of review categorization job is demonstrated by these encouraging findings.

Text Classification on Customer Feedback: A Systematic Literatures Review

“…Dataset size [23], [25], [29], [31], [44], [45] <5,000 [20], [22], [26], [30], [43], [46] 5,000-20,000 [24], [36], [38], [47] 20,000-50,000 [21] 50,000-100,000 [33], [34], [35], [40] >100,000 [27], [28], [32], [37], [39], [41], [42] Not available…”

“…Performance metric [21], [25], [41], [47] Accuracy [28], [32] Accuracy, precision, recall [30], [44] Accuracy, F1-score [20], [23], [24], [26], [29], [31], [36], [37] Accuracy, precision, recall, F1-score [22], [27] Precision, recall, F1-score [38] F1-score [34] Perception score [46] Mapping quality characteristic [40] Relationship (online reviews-hotel sales) [43] Classify user component [33] Confusion matrix Consequently, it is deemed proficient at classifying forthcoming values. According to the data presented in Table 8, it can be observed that the random forest technique exhibits the highest level of accuracy among the ML algorithms, achieving a remarkable accuracy rate of 99.2% when applied to the TripAdvisor dataset.…”

A Survey of Sentiment Analysis and Sarcasm Detection

“…Londhe et al [10] propose a system that combines Long Short-Term Memory (LSTM), fuzzy logic, and incremental learning for sentiment analysis in restaurant reviews. The system effectively handles multi-category classification and captures cross-category correlations by incorporating binary classifiers and an ensemble approach.…”

“…Sentiment analysis [1,2], crucial for understanding public opinions on products and events, has become vital with the internet's growth, but analyzing vast online data poses challenges. The goal [3][4][5][6][7][8][9][10][11] is to assess sentiment, often on a positive-negative spectrum for written reviews. Sentiment analysis, situated at the intersections of computational linguistics, NLP, and data mining, utilizes techniques from these fields to extract sentiments from text.…”

Enhancing Minority Sentiment Classification in Gastronomy Tourism: A Hybrid Sentiment Analysis Framework With Data Augmentation, Feature Engineering and Business Intelligence

“…In contrast, the selection of domain-specific models features Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and CNN-LSTM. The LSTM has been employed by(Londhe & Rao, 2022) to overcome the issues of short text classification in the restaurant sentiment analysis on Yelp dataset and(Khruahong et al, 2022) to analyze the relationship between social media use and its effect on community-based tourism in Thailand. On the other hand, CNN was used by(Mamatha et al, 2022) to deal with problem of review images sentiment classification for restaurants.…”