Yelp Dataset Challenge: Review Rating Prediction

By Nabiha Asghar. 2016

228 citations recorded by the source corpus.

Review websites, such as TripAdvisor and Yelp, allow users to post online reviews for various businesses, products and services, and have been recently shown to have a significant influence on consumer shopping behaviour. An online review typically consists of free-form text and a star rating out of 5. The problem of predicting a user's star rating for a product, given the user's text review for that product, is called Review Rating Prediction and has lately become a popular, albeit hard, problem in machine learning. In this paper, we treat Review Rating Prediction as a multi-class classification problem, and build sixteen different prediction models by combining four feature extraction methods, (i) unigrams, (ii) bigrams, (iii) trigrams and (iv) Latent Semantic Indexing, with four machine learning algorithms, (i) logistic regression, (ii) Naive Bayes classification, (iii) perceptrons, and (iv) linear Support Vector Classification. We analyse the performance of each of these sixteen models to come up with the best model for predicting the ratings from reviews. We use the dataset provided by Yelp for training and testing the models.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science, Natural Language Processing

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Citations in the corpus

Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models (2021, background): “We believe our approach is more effective than manual creation (although both are beneficial): in 7The absolute error rate drops for at least 5 points, with a relative difference of more than 10%. ments. m-polyjuice maintains the in-domain and out-of-domain accuracies on reviews (SST-2, Amzbook, Yel”
Towards Explainable NLP: A Generative Explanation Framework for Text Classification (2018, methodology): “Unfortunately, existing text classiﬁcation models typically utilize the original review texts to predict the overall results (Tang et al., 2015b; Asghar, 2016; Xu et al., 2017) directly, but none of them provides ﬁne-grained explanations about why the decisions are made.”
LSTM and GRU Neural Network Performance Comparison Study: Taking Yelp Review Dataset as an Example (2020, background): “Users can rate merchants, write reviews, and exchange service experiences on it[6][7].”