SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant Reviews

TL;DR

This paper introduces SentiLSTM, a deep learning model using BiLSTM for sentiment analysis of restaurant reviews, achieving high accuracy in classifying reviews as positive or negative.

Contribution

The paper presents a novel deep learning approach with BiLSTM for sentiment classification of restaurant reviews, outperforming traditional machine learning methods.

Findings

BiLSTM achieved 91.35% accuracy on review classification.

Constructed a dataset of 8435 restaurant reviews for evaluation.

BiLSTM outperforms other machine learning algorithms in sentiment analysis.

Abstract

The amount of textual data generation has increased enormously due to the effortless access of the Internet and the evolution of various web 2.0 applications. These textual data productions resulted because of the people express their opinion, emotion or sentiment about any product or service in the form of tweets, Facebook post or status, blog write up, and reviews. Sentiment analysis deals with the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude toward a particular topic is positive, negative, or neutral. The impact of customer review is significant to perceive the customer attitude towards a restaurant. Thus, the automatic detection of sentiment from reviews is advantageous for the restaurant owners, or service providers and customers to make their decisions or services more satisfactory. This paper proposes, a deep learning-based technique (i.e., BiLSTM) to classify the reviews provided by the clients of the restaurant into positive and negative polarities. A corpus consists of 8435 reviews is constructed to evaluate the proposed technique. In addition, a comparative analysis of the proposed technique with other machine learning algorithms presented. The results of the evaluation on test dataset show that BiLSTM technique produced in the highest accuracy of 91.35%.