This page provides data and associated documentation for this talk:

Christopher Potts. 2011. Developing adjective scales from user-supplied textual metadata. NSF Workshop on Restructuring Adjectives in WordNet. Arlington,VA, September 30–Oct 1.

The goal of the talk is to develop and evaluate methods for using naturally occurring metadata (star ratings on service and product reviews) to inform WordNet annotators in constructing modifier scales.

File (zipped CSV file): wn-asr-multicorpus.csv.zip

Column name | Explanation | |
---|---|---|

1 | Word | In the format WORD/tag where tag is a or r |

2 | Rating | 1..10 for IMDB; 1..5 for the other corpora |

3 | Category | Rating on the scale -0.5..0.5 |

4 | Count | Token count for Word in reviews with Rating in Corpus |

5 | Total | Total token count for words in reviews with Rating in Corpus |

6 | Corpus | IMDB, Goodreads, OpenTable, Amazon/Tripadvisor |

File (zipped CSV file): wn-asr-multilevel-assess.csv.zip

Column name | Explanation | |
---|---|---|

1 | Word | In the format WORD/tag where tag is a or r |

2-5 | fit1.coef1, fit1.coef1.p, fit1.coef2, fit1.coef2.p | The linear model coefficients with associated p-values; the fitted values can be obtained with `invlogit(fit1.coef1 + fit1.coef2*x) ` |

6-8 | fit1.aic, fit1.bic, fit1.loglik | Values for assessing the goodness of fit for the linear model (Akaike Information Criterion, Bayesian Information Criterion, Log-Likelihood) |

9-14 | fit2.coef1, fit2.coef1.p, fit2.coef2, fit2.coef2.p, fit2.coef3, fit2.coef3.p | The quadratic model coefficients with associated p-values; the fitted values can be obtained with `invlogit(fit1.coef1 + fit1.coef2*x + fit1.coef2*x` |

15-17 | fit2.aic, fit2.bic, fit2.loglik | Values for assessing the goodness of fit for the quadratic model (Akaike Information Criterion, Bayesian Information Criterion, Log-Likelihood) |

18 | Inquirer | The Harvard Inquirer classification: Positiv, Negativ, Neutral; NA iff the word is not in the Harvard Inquirer |

19 | SentiWordNetPositive | The SentiWordNet positive score: [0-1] or NA iff the word is not in SentiWordNet |

20 | SentiWordNetNegative | The SentiWordNet negative score: [0-1] or NA iff the word is not in SentiWordNet |

21 | SentiWordNetPolarity | positive if SentiWordNetPositive > SentiWordNetNegative; negative if SentiWordNetPositive < SentiWordNetNegative, else neutral; NA iff the word is not in SentiWordNet |

22 | MicroWNOpPositive | The MicroWNOp positive score: [0-1] or NA iff the word is not in MicroWNOp |

23 | MicroWNOpNegative | The MicroWNOp negative score: [0-1] or NA iff the word is not in MicroWNOp |

24 | MicroWNOpPolarity | positive if MicroWNOpPositive > MicroWNOpNegative; negative if MicroWNOpPositive < MicroWNOpNegative, else neutral; NA iff the word is not in MicroWNOpNegative |

25 | MqapPolarity | positive, negative, or neutral; NA iff the word is not in the MQAP subjectivity lexicon |

26 | MqapStrength | 1 if the strength is weaksubj; 2 if the strength is strongsubj; NA iff the word is not in the MQAP subjectivity lexicon |

27 | Predicted | If Model == Linear, then positive if fit1.coef2 (column 4) is ≥ 0, else negative; if Model == Quadratic, then positive if fit2.coef2 (column 11) is ≥ 0, else negative; if Model == None, then neutral |

28 | Model | Values: Linear, Quadratic, None. The preferred model choice: if only one is significant, then it is chosen; if both are significant, then we pick the one with the greater log-likelihood (columns 8 and 17); if neither model is significant, then we choose None. Throughout, the p-value threshold is < 0.05. |

29 | RawScore | fit1.coef2 (column 4) if Model == Linear; fit2.coef2 (column 11) if Model == Quadratic; else 0 |

30 | NormedScore | RawScore z-score adjusted relative to the population of significant coefficients for fit1.coef2 or fit2.coef2, depending on which value is in RawScore |

File (zipped CSV file): wn-asr-multilevel-cmp.csv.zip

1. | Word | In the format WORD/tag where tag is a or r |

2 | SimWord | In the format WORD/tag where tag is a or r; this word is related to Word via the WordNet similar_to relation |

3 | Polarity | The polarity assigned both by the MPQA subjectivity lexicon and the method proposed in the talk. (We limit attention to pairs where this category value is agreed upon; the classification experiments assess the agreement level for this problem.) |

4 | MqapWordStrength | The MPQA strength for Word: 1 == weaksubj; 2 == strongsubj |

5 | MqapSimStrength | The MPQA strength for SimWord: 1 == weaksubj; 2 == strongsubj |

6 | WordScore | Our predicted score for Word; same as NormedScore from wn-asr-multilevel-assess.csv |

7 | SimScore | Our predicted score for SimWord; same as NormedScore from wn-asr-multilevel-assess.csv |

8 | MqapCmp | Comparison value from MPQA: stronger if MqapWordStrength > MqapSimStrength; weaker if MqapWordStrength < MqapSimStrength; same otherwise |

9 | PredictedCmpInformal | Comparison value for our informal method: stronger if WordScore > SimScore; weaker if WordScore < SimScore; same otherwise |

10-11 | category.coef, category.p | Coefficient and p-value for the basic Category predictor in the comparison model |

12-13 | interaction.coef, interaction p | Coefficient and p-value for the interaction term Category*Stronger in the comparison model |

14 | PredictedCmpFormal | if category.p ≥ 0.05 or interaction p ≥ 0.05, same; else if sign(category.coef) == sign(interaction.coef), stronger; else weaker |