Comparative Experiments on Sentiment Classification for Online Product Reviews

By Hang Cui, Vibhu Mittal, Mayur Datar. AAAI Conference on Artificial Intelligence, 2006

373 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A survey on sentiment analysis by using machine learning methods (2017, unknown): “Hang Cui [5] et al.”
Computational approaches for mining user's opinions on the Web 2.0 (2014, background): “Normalization approaches (basic resp. strong normalization) of noisy texts offer only small improvements, but are helpful and have to be further developed.”
Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts (2016, methodology): “(2002) and Cui et al. (2006) used n-grams and POS tags and applied them to NB, ME and SVM classifiers.”
Designing novel review ranking systems: predicting the usefulness and impact of reviews (2007, unknown): “view [4, 6, 11, 12, 14, 18–24].”
Discriminative Neural Sentence Modeling by Tree-Based Convolution (2015, background): “Feature engineering—for example, n-gram features (Cui et al., 2006), dependency subtree features (Nakagawa et al., 2010), or more dedicated ones (Silva et al., 2011)—can play an important role in modeling sentences.”
Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics (2011, methodology): “For each review, we retrieve the actual textual content of the review and the review rating of the product given by the reviewer.”
Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges (2023, background): “[56] with different machine learning classifiers on the product reviews dataset.”