Why Do Document-Level Polarity Classifiers Fail?
Machine learning solutions are often criticized for the lack of explanation of their successes and failures. Understanding which instances are misclassified and why is essential to improve the learning process. This work helps to fill this gap by proposing a methodology to characterize, quantify and measure the impact of hard instances in the task of polarity classification of movie reviews. We characterize such instances into two categories: neutrality, where the text does not convey a clear polarity, and discrepancy, where the polarity of the text is the opposite of its true rating. We quantify the number of hard instances in polarity classification of movie reviews and provide empirical evidence about the need to pay attention to such problematic instances, as they are much harder to classify, for both machine and human classifiers. To the best of our knowledge, this is the first systematic analysis of the impact of hard instances in polarity detection from well-formed textual reviews.
Code (1)
Similar Papers 제목 키워드 기반
Linguistic features for sentence difficulty prediction in ABSA
One of the challenges of natural language understanding is to deal with the subjectivity of sentences, which may express opinions and emotions that add layers of complexity and nuance. Sentiment analysis is a field that …
Aspect-Based Sentiment AnalysisDiversityNatural Language UnderstandingPrediction+3Bootstrapping Sentiment Labels For Unannotated Documents With Polarity PageRank
We present a novel graph-theoretic method for the initial annotation of high-confidence training data for bootstrapping sentiment classifiers. We estimate polarity using topic-specific PageRank. Sentiment information is …
Document ClassificationGeneral ClassificationSentiment AnalysisA Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification
In this paper, we propose a variational approach to weakly supervised document-level multi-aspect sentiment classification. Instead of using user-generated ratings or annotations provided by domain experts, we use target…
General ClassificationSentiment AnalysisSentiment ClassificationReview-Level Sentiment Classification with Sentence-Level Polarity Correction
We propose an effective technique to solving review-level sentiment classification problem by using sentence-level polarity correction. Our polarity correction technique takes into account the consistency of the polariti…
BIG-bench Machine LearningClassificationGeneral ClassificationSentence+2Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning
Domain-adapted sentiment classification refers to training on a labeled source domain to well infer document-level sentiment on an unlabeled target domain. Most existing relevant models involve a feature extractor and a …
ClassificationGeneral ClassificationSentiment AnalysisSentiment Classification