|
| Authors | Models | Data source | Method | Attributes type | Accuracy (percent) |
|
| Taboada and Grieve [54] | Lexicon-based | Reviews | Taking into account text structure and adjectives frequency | Attitude | N/A |
|
| Whitelaw et al. [55] | Lexicon-based | Movie reviews | Analyzing appraisal adjectives and modifiers | Attitude, orientation, graduation, polarity | 90.2 |
|
| Read and Carroll [56] | Lexicon-based | Book reviews | Measuring interannotator agreement | Attitude, engagement, graduation, polarity | N/A |
|
| Argamon et al. [57] | NB, SVM | Documents | Automatic determining complex sentiment-related attributes | Attitude; orientation; force | N/A |
|
| Balahur et al. [58] | Robert Plutchik’s wheel of emotion; Parrot’s tree-structured list of emotions | ISEAR Corpus | EmotiNet (EmotiNet defines to store action chains and their corresponding emotional labels from several situations; in such a way, authors could be able to extract general patterns of appraisal) | Actor, action, object | N/A |
| Emotion |
|
| Bloom [59] | Lexicon-based | MPQA 2.0 Corpus; UIC Review Corpus; Darmstadt Service Review Corpus; JDPA Sentiment Corpus; IIT Sentiment Corpus | Functional local appraisal grammar extractor | Attitude | 44.6 |
|
| Khoo et al. [50] | Lexicon-based | Political news article | Analyzing appraisal groups | Actor, attitude, engagement, polarity | N/A |
|
| Korenek and Šimko [60] | SVM | Microblog posts | Structuring sentiment analysis based on appraisal theory | Attitude, graduation, polarity, orientation, engagement | 87.57 |
|
| Cui and Shibamoto-Smith [61] | Lexicon-based | Self-constructed corpus of Chinese newspapers and websites | Discovering the lexical, syntactic, and semantic features of different types of sentiment parameters | Attitude | N/A |
| Peripheral sentiment parameters (topic, source, field, process, degree) |
|
