Sentiment Analysis of Restaurant Review with Classification Approach in the Decision Tree-J48 Algorithm

Adnan, Masrur; Sarno, Riyanarto; Sungkono, Kelly Rossa

doi:10.1109/isemantic.2019.8884282

Cited by 11 publications

(3 citation statements)

References 11 publications

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“…In the evaluations or comments on this Web site, Adnan et al [6] intended to seek good or negative judgments and assessed the results of the process, especially in 'Surabaya' restaurant. These observations or reviews are in the form of text or word information.…”

Section: Literature Reviewmentioning

confidence: 99%

Bangla News Classification Using GloVe Vectorization, LSTM, and CNN

Chowdhury

Eumi

Sarkar

et al. 2021

Lecture Notes on Data Engineering and Communications Technologies

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Text mining has gained considerable popularity over the last few years. Since the awareness of mobile media, streaming media, print media, and many other outlets are now accessible to consumers. Due to the large availability of text in many respects, research experts registered many unstructured data and found various ways in the literature of converting this dispersed text into a given, organized volume. Compared to the short text, the emphasis on complete classification (complete news, big records, long text, etc.) is prevalent. We addressed in this paper the process of text classification, grading, and various methodologies for feature extraction in short texts, i.e., news classification based on their headlines. Existing classification is compared and their operating methodologies presented efficiently. This work serves the purpose of classifying different types of Bangla Newspaper articles into 10 specific categories. The classification task is performed on Bengali text from three renowned newspapers of Kolkata. We have used advanced data tokenization techniques and unsupervised 'GloVe' vectorization for better classification performance. We applied LSTM and CNN as our main feature extractors. Comparing with other models like binary SVM classifier, standard LSTM, BiLSTM, CNN, or ANN, this proposed work gives better accuracy of 87%.

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Section: Literature Reviewmentioning

confidence: 99%

Bangla News Classification Using GloVe Vectorization, LSTM, and CNN

Chowdhury

Eumi

Sarkar

et al. 2021

Lecture Notes on Data Engineering and Communications Technologies

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“…A data analysis or statement on the TripAdvisor's web page about products in restaurants in Surabaya is used as a source in this paper [4]. The author Masrur Adnan et.…”

Section: Literature Surveymentioning

confidence: 99%

Food Reviews Classification using multi-label convolutional neural network text classifier

Lasne¹,

Nandrekar²,

Khan³

et al. 2021

ITM Web Conf.

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Most commercial websites, such as Amazon, encourage users to leave reviews of the goods and services they get after purchasing them. For certain consumers, this analysis is critical when determining whether or not to buy a product. Understanding the consequences of feedback and correctly classifying their utility may therefore be an advantageous method for websites. The classification results can also be used as a review and recommendation program for ongoing success. Nowadays people visit several restaurants on various occasions. They get confused most of the time after having a look at the food menu. Based on the ratings and reviews of the dish it becomes easier for them to decide the dish they wish to order. However they unable to read each review of the previous customers. So to overcome this issue, we have proposed NLP (Natural Language Processing) technique and Spacy CNN (Convolutional Neural Network) pipeline system which will classify all the reviews in a single rating. Each review is labelled with a reviewer's score indicating the sentiment of the reviewers. Our task is to predict a reviewer’s score on a scale of 0 or 1. Where 1 indicates the users like the dish while 0 indicates that the reviewers were not satisfied with the dish.

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“…Classifying sentiments in restaurant reviews are a prominent problem in the NLP area [6][7][8], as it demands substantial annotated data and computational resources. Automated SA can help monitor restaurant reputation, identify common customer issues, improve restaurant brand monitoring, influence business decision making, and improve products and services.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing

Branco,

Parada,

Silva

et al. 2024

Electronics

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This study focuses on improving sentiment analysis in restaurant reviews by leveraging transfer learning and transformer-based pre-trained models. This work evaluates the suitability of pre-trained deep learning models for analyzing Natural Language Processing tasks in Portuguese. It also explores the viability of utilizing edge devices for Natural Language Processing tasks, considering their computational limitations and resource constraints. Specifically, we employ bidirectional encoder representations from transformers and robustly optimized BERT approach, two state-of-the-art models, to build a sentiment review classifier. The classifier’s performance is evaluated using accuracy and area under the receiver operating characteristic curve as the primary metrics. Our results demonstrate that the classifier developed using ensemble techniques outperforms the baseline model (from 0.80 to 0.84) in accurately classifying restaurant review sentiments when three classes are considered (negative, neutral, and positive), reaching an accuracy and area under the receiver operating characteristic curve higher than 0.8 when examining a Zomato restaurant review dataset, provided for this work. This study seeks to create a model for the precise classification of Portuguese reviews into positive, negative, or neutral categories. The flexibility of deploying our model on affordable hardware platforms suggests its potential to enable real-time solutions. The deployment of the model on edge computing platforms improves accessibility in resource-constrained environments.

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Sentiment Analysis of Restaurant Review with Classification Approach in the Decision Tree-J48 Algorithm

Cited by 11 publications

References 11 publications

Bangla News Classification Using GloVe Vectorization, LSTM, and CNN

Bangla News Classification Using GloVe Vectorization, LSTM, and CNN

Food Reviews Classification using multi-label convolutional neural network text classifier

Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing

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