Senti-Lexicon and Analysis for Restaurant Reviews of Myanmar Text

Aye, Yu Mon; Aung, Sint Sint

doi:10.22161/ijaems.4.5.7

Cited by 10 publications

(5 citation statements)

References 5 publications

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“…The tweets were divided into twelve classes, and the result is that food delivery service gain 83 total number of tweets: 10 positive tweets and 73 negative tweets. In [41], they compared 1113 opinion words manually extracted which contain 38 emoticons from 500 review icons randomly selected from 800 reviews. They extract 38 emoticons in their experiment and 977 opinion words specific in customer feedback, while another 66 opinions word cannot be extract and 98 opinions were incorrect.…”

Section: Resultsmentioning

confidence: 99%

Customer reviews analytics on food delivery services in social media: a review

Shaeeali

Mohamed

Mutalib

2020

IJ-AI

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Food delivery services have gained attention and become a top priority in developed cities by reducing travel time and waiting time by offering online food delivery options for a variety of dishes from a wide variety of restaurants. Therefore, customer analytics have been considered in business analysis by enabling businesses to collect and analyse customer feedback to make business decisions to be more advanced in the future. This paper aims to study the techniques used in customer analytics for food delivery services and identify the factors of customers’ reviews for food delivery services especially in social media. A total of 53 papers reviewed, several techniques and algorithms on customer analytics for food delivery services in social media are Lexicon, machine learning, natural language processing (NLP), support vector machine (SVM), and text mining. The paper further analyse the challenges and factors that give impacts to the customers’ reviews for food delivery services. These findings would be appropriate for development and enhancement of food delivery services in future works.

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Section: Resultsmentioning

confidence: 99%

Customer reviews analytics on food delivery services in social media: a review

Shaeeali

Mohamed

Mutalib

2020

IJ-AI

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“…This study found that the attributes derived from previous studies such as food, service, ambiance, and price were not enough to affect restaurants' ratings and that context should be added as a significant attribute. Meanwhile, Aye and Aung [1] proposed a Myanmar language resource for lexicon-based SA as a solution to language-specific problems since most studies have considered the English language for SA. Restaurant review data were used, but informal expressions were not addressed.…”

Section: Related Workmentioning

confidence: 99%

“…T He popularity of social media websites has witnessed tremendous growth in the last few years [1]. Social media sites have grown not only in terms of volume but also in their importance to different aspects of life, including business, politics, and education [2].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

et al. 2022

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Online media has an increasing presence on the restaurants' activities through social media websites, coinciding with an increase in customers' reviews of these restaurants. These reviews become the main source of information for both customers and decision-makers in this field. Any customer who is seeking such places will check their reviews first, which usually affect their final choice. In addition, customers' experiences can be enhanced by utilizing other customers' suggestions. Consequently, customers' reviews can influence the success of restaurant business since it is considered the final judgment of the overall quality of any restaurant. Thus, decision-makers need to analyze their customers' underlying sentiments in order to meet their expectations and improve the restaurants' services, in terms of food quality, ambiance, price range, and customer service. The number of reviews available for various products and services has dramatically increased these days and so has the need for automated methods to collect and analyze these reviews. Sentiment Analysis (SA) is a field of machine learning that helps analyze and predict the sentiments underlying these reviews. Usually, SA for customers' reviews face imbalanced datasets challenge, as the majority of these sentiments fall into supporters or resistors of the product or service. This work proposes a hybrid approach by combining the Support Vector Machine (SVM) algorithm with Particle Swarm Optimization (PSO) and different oversampling techniques to handle the imbalanced data problem. SVM is applied as a machine learning classification technique to predict the sentiments of reviews by optimizing the dataset, which contains different reviews of several restaurants in Jordan. Data were collected from Jeeran, a well-known social network for Arabic reviews. A PSO technique is used to optimize the weights of the features, as well as four different oversampling techniques, namely, the Synthetic Minority Oversampling Technique (SMOTE), SVM-SMOTE, Adaptive Synthetic Sampling (ADASYN) and borderline-SMOTE were examined to produce an optimized dataset and solve the imbalanced problem of the dataset. This study shows that the proposed PSO-SVM approach produces the best results compared to different classification techniques in terms of accuracy, F-measure, G-mean and AUC, for different versions of the datasets.

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“…However, numerical ratings can be used as indicators of sentiment (Pang et al, 2002;Racherla et al, 2013;Dave, 2003;Turney, 2002). There are two approaches to sentiment analysis of text reviews (Aye & Aung, 2018). The lexicon-based approach compares individual words from the sentences with the sentiment words listed in lexicons in order to determine whether the words used in a review convey any sentiment (positive or negative) or not.…”

Section: Approaches To Sentiment Analysismentioning

confidence: 99%

Contemporary Data Analysis Techniques for Online Reputation Management in Hospitality and Tourism

Grljević

Bošnjak

2019

FU Econ Org

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Knowing what attracts or deters tourists to/from a tourist visit and what products to offer them and to pay special attention to is crucial for good economic results. Such knowledge can be obtained by analysis of online comments and reviews that tourists leave on travel websites (such as Booking, TripAdvisor, Trivago, etc.). This paper describes the value which information about opinions and emotions hidden in online reviews has for managers who receive it, especially the knowledge of (dis)satisfaction of users with certain aspects of the tourist offer. Uncovered knowledge from online reviews provides a chance to take advantage of the strong points, and correct the shortcomings through timely corrective measures and actions. Contemporary approaches and methods of analyzing online reviews and the opportunities for development they provide in the tourism industry are described through a case study conducted over a subset of 20491 hotel reviews from TripAdvisor. We have conducted sentiment analysis of reviews with the goal of building an automated model which will successfully distinguish positive from negative reviews. Logistic Regression classifier has the best performance, in 90% of reviews it has correctly classified positive reviews and in 83% negative. We have illustrated how association rules can help management to uncover relationships between concepts under discussion in negative and positive reviews.

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Senti-Lexicon and Analysis for Restaurant Reviews of Myanmar Text

Abstract: Abstract-

Cited by 10 publications

References 5 publications

Customer reviews analytics on food delivery services in social media: a review

Customer reviews analytics on food delivery services in social media: a review

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

Contemporary Data Analysis Techniques for Online Reputation Management in Hospitality and Tourism

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