Abstract
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, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging.
Citation impact
7,017
total citations
- FWCI
- 42.64
- Percentile
- 100%
- References
- 35
Citations per year
Authors
3Topics & keywords
Topics
Keywords
- Computer science
- Naive Bayes classifier
- Support vector machine
- Categorization
- Artificial intelligence
- Sentiment analysis
- Machine learning
- Principle of maximum entropy
UN Sustainable Development Goals
- Quality Education
No related works found for this paper.