SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant Reviews
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%.
Code (1)
Tasks
Sentiment AnalysisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
GERestaurant: A German Dataset of Annotated Restaurant Reviews for Aspect-Based Sentiment Analysis
We present GERestaurant, a novel dataset consisting of 3,078 German language restaurant reviews manually annotated for Aspect-Based Sentiment Analysis (ABSA). All reviews were collected from Tripadvisor, covering a diver…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Aspect Category DetectionAspect Category Sentiment Analysis+1Sentiment Analysis in Scholarly Book Reviews
So far different studies have tackled the sentiment analysis in several domains such as restaurant and movie reviews. But, this problem has not been studied in scholarly book reviews which is different in terms of review…
Sentiment AnalysisRude waiter but mouthwatering pastries! An exploratory study into Dutch Aspect-Based Sentiment Analysis
The fine-grained task of automatically detecting all sentiment expressions within a given document and the aspects to which they refer is known as aspect-based sentiment analysis. In this paper we present the first full …
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)General ClassificationSentiment Analysis+1Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology using Natural Language Processing and Deep Learning for Smarter Decision Aid. Case study of restaurant choice using TripAdvisor reviews
Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert eva…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Decision MakingSentiment AnalysisSentiment Analysis: Predicting Yelp Scores
In this work, we predict the sentiment of restaurant reviews based on a subset of the Yelp Open Dataset. We utilize the meta features and text available in the dataset and evaluate several machine learning and state-of-t…
Sentiment Analysis