Abstract
We present a graph-based semi-supervised learning algorithm to address the sentiment analysis task of rating inference. Given a set of documents (e.g., movie reviews) and accompanying ratings (e.g., "4 stars"), the task calls for inferring numerical ratings for unlabeled documents based on the perceived sentiment expressed by their text. In particular, we are interested in the situation where labeled data is scarce. We place this task in the semi-supervised setting and demonstrate that considering unlabeled reviews in the learning process can improve rating-inference performance. We do so by creating a graph on both labeled and unlabeled data to encode certain assumptions for this task. We then solve an optimization problem to obtain a smooth rating function over the whole graph. When only limited labeled data is available, this method achieves significantly better predictive accuracy over other methods that ignore the unlabeled examples during training.
Keywords
Computer scienceInferenceGraphArtificial intelligenceSentiment analysisENCODETask (project management)Machine learningLabeled dataSet (abstract data type)Data setNatural language processingTheoretical computer science
Affiliated Institutions
Related Publications
Semi-Supervised Classification of Network Data Using Very Few Labels
The goal of semi-supervised learning (SSL) methods is to reduce the amount of labeled training data required by learning from both labeled and unlabeled instances. Macskassy and...
A unified architecture for natural language processing
We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named enti...
Seeing stars
We address the rating-inference problem, wherein rather than simply decide whether a review is "thumbs up" or "thumbs down", as in previous sentiment analysis work, one must det...
Thumbs up?
We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data,...
Graph Convolutional Networks for Text Classification
Text classification is an important and classical problem in natural language processing. There have been a number of studies that applied convolutional neural networks (convolu...
Publication Info
- Year
- 2006
- Type
- article
- Pages
- 45-45
- Citations
- 317
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
317
OpenAlex
Cite This
Andrew B. Goldberg,
Xiaojin Zhu
(2006).
Seeing stars when there aren't many stars.
, 45-45.
https://doi.org/10.3115/1654758.1654769
Identifiers
- DOI
- 10.3115/1654758.1654769