A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
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Summary
A novel machine-learning method is proposed that applies text-categorization techniques to just the subjective portions of the document, which greatly facilitates incorporation of cross-sentence contextual constraints.
- Type
- article
- Published
- 2004-07-21
- Cited by
- 4,195
- References
- 28
- Access
- Open access
- OpenAlex
- https://openalex.org/W2114524997
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:388
Keywords
Computer science, Categorization, Sentiment analysis, Polarity (international relations), Sentence
References
- A system for affective rating of texts
- Opinion Classification Through Information Extraction
- Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
- Exploring attitude and affect in text : theories and applications : papers from the 2004 AAAI Symposium, March 22-24, Stanford, California
- Learning from Labeled and Unlabeled Data using Graph Mincuts
- Tracking Point of View in Narrative
- Mining product reputations on the Web
- Network Flows: Theory, Algorithms, and Applications
- Mining newsgroups using networks arising from social behavior
- A model of textual affect sensing using real-world knowledge
- Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences
- Learning Extraction Patterns for Subjective Expressions
- Combining Low-Level and Summary Representations of Opinions for Multi-Perspective Question Answering
- Learning subjective nouns using extraction pattern bootstrapping
- Affect analysis of text using fuzzy semantic typing
- Transductive Learning via Spectral Graph Partitioning
- Mining the peanut gallery: opinion extraction and semantic classification of product reviews
- Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques
- Fast approximate energy minimization via graph cuts
- Thumbs up? Sentiment Classification using Machine Learning Techniques
Cited by
- Weakly-supervised Appraisal Analysis
- Basic Units for Chinese Opinionated Information Retrieval
- A literature survey of methods for analysis of subjective language
- Using a Multimodal Sensing Approach to Characterize Human Responses to Affective and Deceptive States
- Linked Opinions: Describing Sentiments on the Structured Web of Data
- Feature-based transfer learning with real-world applications
- High-level Features for Learning Subjective Language across Domains
- Building Tagged Linguistic Unit Databases for Sentiment Detection
- Unsupervised and knowledge-poor approaches to sentiment analysis
- Sentiment Analysis Using Common-Sense and Context Information
- Discourse-level relations for opinion analysis
- Seeking Variety: A Dynamic Model of Employee Blog Reading Behavior
- Is This Review Believable? A Study of Factors Affecting the Credibility of Online Consumer Reviews from an ELM Perspective
- Sentiment Learning from Imbalanced Dataset: An Ensemble Based Method
- SentiWordNet: A High-Coverage Lexical Resource for Opinion Mining
- SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining
- A survey on the role of negation in sentiment analysis
- Can You Trust Online Ratings? Evidence of Systematic Differences in User Populations
- Semantic Tagging at the Sense Level
- Combining Granularity-based Topic-Dependent and Topic-Independent Evidences for Opinion Detection
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