AM: textual attitude analysis model
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Recognition of affect, judgment, and appreciation in text
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Ensemble of feature sets and classification algorithms for sentiment classification
Information Sciences: an International Journal
Affect analysis model: Novel rule-based approach to affect sensing from text
Natural Language Engineering
Identification of fine grained feature based event and sentiment phrases from business news stories
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Deciphering word-of-mouth in social media: Text-based metrics of consumer reviews
ACM Transactions on Management Information Systems (TMIS)
Unifying local and global agreement and disagreement classification in online debates
WASSA '12 Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis
Product Comparison Networks for Competitive Analysis of Online Word-of-Mouth
ACM Transactions on Management Information Systems (TMIS)
Image and Vision Computing
Combining user preferences and user opinions for accurate recommendation
Electronic Commerce Research and Applications
Using Generalized Annotated Programs to Solve Social Network Diffusion Optimization Problems
ACM Transactions on Computational Logic (TOCL)
Sentiment classification: The contribution of ensemble learning
Decision Support Systems
Activity-based topic discovery
Web Intelligence and Agent Systems
| Hi-index | 0.00 |
Most research on determining the strength of subjective expressions in a sentence or document uses single, specific parts of speech such as adjectives, adverbs, or verbs. To date, almost no research covers the development of a single comprehensive framework in which we can analyze sentiment that takes all three into account. The authors propose the AVA (adjective verb adverb) framework for identifying opinions on any given topic. In AVA, a user can select any topic t of interest and any document d. AVA will return a score that d expresses topic t. The score is expressed on a –1 (maximally negative) to +1 (maximally positive) scale.