Sentiment Analysis of Restaurant Reviews Using Machine Learning Techniques

Krishna, Akshay; Akhilesh, V.; Aich, Animikh; Hegde, Chetana

doi:10.1007/978-981-13-5802-9_60

Cited by 43 publications

(19 citation statements)

References 11 publications

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“…Researches show that NLP, data mining, and text classification can be very helpful in every prospect of life. There are also many other researchers who have used NLP in hate speech, sentiment analysis [ 2 ], detection of controversial Urdu speeches [ 3 ], movie reviews [ 4 ], stock market [ 5 ], online reviews [ 6 ], and restaurant reviews [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification

Qasim

Bangyal

Alqarni

et al. 2022

Journal of Healthcare Engineering

108

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Text Classification problem has been thoroughly studied in information retrieval problems and data mining tasks. It is beneficial in multiple tasks including medical diagnose health and care department, targeted marketing, entertainment industry, and group filtering processes. A recent innovation in both data mining and natural language processing gained the attention of researchers from all over the world to develop automated systems for text classification. NLP allows categorizing documents containing different texts. A huge amount of data is generated on social media sites through social media users. Three datasets have been used for experimental purposes including the COVID-19 fake news dataset, COVID-19 English tweet dataset, and extremist-non-extremist dataset which contain news blogs, posts, and tweets related to coronavirus and hate speech. Transfer learning approaches do not experiment on COVID-19 fake news and extremist-non-extremist datasets. Therefore, the proposed work applied transfer learning classification models on both these datasets to check the performance of transfer learning models. Models are trained and evaluated on the accuracy, precision, recall, and F1-score. Heat maps are also generated for every model. In the end, future directions are proposed.

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Section: Introductionmentioning

confidence: 99%

A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification

Qasim

Bangyal

Alqarni

et al. 2022

Journal of Healthcare Engineering

108

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show abstract

“…It concluded the study by indicating that Rule Induction (RI) is the most frequently used ML technique in data mining [9] [10]. The previous study on sales prediction have been performed using regression techniques as well as boosting techniques and boosting algorithms have resulted in better results as compared to regression techniques [11]. Zhan-Li Sun et.al [12] has used a neural network technique known as Extreme Learning Machine (ELM) to find out the relationship between sales amount and few crucial factors which affect demand using a real time dataset and found that their model outperform over the other sales forecasting methods using back propagation neural networks.…”

Section: Literature Reviewmentioning

confidence: 93%

Comparative Analysis of Supervised Machine Learning Techniques for Sales Forecasting

Raizada¹,

Saini²

2021

IJACSA

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This study talks about how data mining can be used for sales forecasting in retail sales and demand prediction. Prediction of sales is a crucial task which determines the success of any organization in the long run. There are various techniques available for predicting the sales of a supermarket such as Time Series Algorithm, Regression Techniques, Association rule etc. In this paper, a comparative analysis of some of the Supervised Machine Learning Techniques have been done such as Multiple Linear Regression Algorithm, Random Forest Regression Algorithm, K-NN Algorithm, Support Vector Machine (SVM) Algorithm and Extra Tree Regression to build a prediction model and precisely estimate possible sales of 45 retail outlets of Walmart store which are at different geographical locations. Walmart is one of the foremost stores across the world and thus authors would like to predict the sales accurately. Certain events and holidays affect the sales periodically, which sometimes can also be on a daily basis. The forecast of probable sales is based on a combination of features such as previous sales data, promotional events, holiday week, temperature, fuel price, CPI i.e., Consumer Price Index and Unemployment rate in the state. The data is collected from 45 outlets of Walmart and the prediction about the sales of Walmart was done using various Supervised Machine Learning Techniques. The contribution of this paper is to help the business owners decide which approach to follow while trying to predict the sales of their Supermarket taken into account different scenarios including temperature, holidays, fuel price, etc. This will help them in deciding the promotional and marketing strategy for their products.

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“…They used TF-IDF to extracts n-gram features and obtained the accuracy of about 90.15%. Akshay et al [9] explored machine learning classifiers for analyzing sentiment of the restaurant reviews. They have obtained the highest accuracy of 94.5% for their dataset.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naïve Bayes

Hossain

Sharif

Hoque

2021

Advances in Intelligent Systems and Computing

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Recently, sentiment polarity detection has increased attention to NLP researchers due to the massive availability of customer's opinions or reviews in the online platform. Due to the continued expansion of e-commerce sites, the rate of purchase of various products, including books, are growing enormously among the people. Reader's opinions/reviews affect the buying decision of a customer in most cases. This work introduces a machine learning-based technique to determine sentiment polarities (either positive or negative category) from Bengali book reviews. To assess the effectiveness of the proposed technique, a corpus with 2000 reviews on Bengali books is developed. A comparative analysis with various approaches (such as logistic regression, naive Bayes, SVM, and SGD) also performed by taking into consideration of the unigram, bigram, and trigram features, respectively. Experimental result reveals that the multinomial Naive Bayes with unigram feature outperforms the other techniques with 84% accuracy on the test set.

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Sentiment Analysis of Restaurant Reviews Using Machine Learning Techniques

Cited by 43 publications

References 11 publications

A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification

A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification

Comparative Analysis of Supervised Machine Learning Techniques for Sales Forecasting

Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naïve Bayes

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