Enhancing Sentiment Analysis in Restaurant Reviews: A Hybrid Approach Integrating Lexicon-Based Features and LSTM Networks
Keywords:
Bag of Words, Long Short-Term Memory, N-gram, TF-IDF Vectors, Word EmbeddingAbstract
This research investigates sentiment analysis applied to restaurant reviews, employing an innovative hybrid methodology combining traditional lexicon-based features with advanced deep learning techniques, particularly Long Short-Term Memory (LSTM) networks. The study commences with data acquisition and preprocessing, including tokenization, stop word removal, lemmatization, and punctuation removal. Feature extraction incorporates lexicon-based methods and various tokenization techniques, such as TF-IDF vectors, N-gram, Bag of Words, and Word Embedding. The novel aspect lies in the integration of LSTM network-based classification for sentiment analysis. The results showcase the effectiveness of this hybrid approach, with an accuracy of 95.89% and superior performance metrics across sensitivity, specificity, precision, and F1-score. Comparative analysis with previous research work validates the superiority of the proposed methodology. The study also provides insights into training time variations associated with different feature extraction techniques, contributing to a comprehensive understanding of sentiment analysis in the context of restaurant reviews.
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Sentiment Analysis of Restaurant Reviews Dataset. Available online at: https://www.kaggle.com/code/apekshakom/sentiment-analysis-of-restaurant-reviews/notebook
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