Due at 9:30am on July 12
Some handbuilt sentiment dictionaries and most algorithms for automatically building large sentiment lexicons classify simple strings, ignoring grammatical information like part of speech as well as contextual information about where and how the strings were used. Thus, they will likely miss the reliable sentiment contrast between the adjective gross (as in yucky) and the noun gross (as in profits), and the fact that sensitive is likely positive when it describes a novelist but negative when it describes a bruise.
Your task: Find three original examples in which grammatical information or contextual information is important for sentiment classification. For each one, include an example sentence that highlights the contrast you identified. If your examples are from a language other than English, please provide English glosses.
Extend Problem 1 from sentiment (positive-negative) to the more general problem of affect classification. Choose two categories from each of LIWC and the General Inquirer below. From each, select one word in the category and find (or make up) 3 original examples in which the word as selected does not exhibit the sense labeled by the category. (Try to find interesting examples that are likely to really create problems, rather than make use of rare senses of words.)
Hatzivassiloglou and McKeown (1997) use coordinators and other local morpho-syntactic features to predict the polarity of adjectives. This problem asks you to explore and extend some of their ideas. The data file is imdb-adjcoord.csv (download as a ZIP archive), which contains data on over 100,000 coordinated adjective tokens drawn from the user-supplied reviews at IMDB.com. This file can be read with any program that reads tabular data (Excel, OpenOffice, R, SPSS, ...). The format is as follows (with all values comma-separated and no value containing a comma itself):
| Column | Name | Description |
|---|---|---|
| 1 | PolMatch | 1 if the cols. 3 and 8 values match, else 0. |
| 2 | Adj1 | The left adj in the coordination. |
| 3 | Adj1Pol | The polarity of Adj1 according to the Harvard Inquirer. |
| 4-6 | Adj1Prefix, Adj1Stem, Adj1Suffix | A heuristic parse of Adj1. For example, disinterested → (dis, interest, ed) and unhappy → (un, happy, ) |
| 7 | Adj2 | The right adj in the coordination. |
| 8 | Adj2Pol | The polarity of Adj2 according to the Harvard Inquirer. |
| 9-11 | Adj2Prefix, Adj2Stem, Adj2Suffix | A heuristic parse of Adj2. |
| 12 | BaseMatch | 1 if col. 5 matches col. 10, else 0 |
| 13 | Coord | The coordinator: and, but, nor, or, versus, or yet. |
| 14 | Conj1Neg | 1 if the first conjunct was negated with not or n't, else 0. (It is generally hard to tell whether such negation scopes over the entire conjunction or just the first conjunct.) |
| 15 | Conj2Neg | 1 if the second conjunct was negated with not, else 0. |
| 16 | ClauseType | The clause type of the containing sentence: DEC(larative); INT(errogative); EXC(clamative), based on the final punctuation tag. |
| 17 | Genre | The genre of the movie under review. |
| 18 | Year | The release year of the movie under review. |
| 19 | Rating | The rating of the review containing this coordination: 1...10. |
| 20 | Helpful | The percentage of registered users who found the review containing this coordination helpful: [0,1]. |
There are lots of hypotheses one can address using this database. For example, a hypothesis based on Hatzivassiloglou and McKeown is that but is a strong indicator of contrasting sentiment, whereas and is a strong indicator of matching sentiment. The following table of counts from the database supports this view:
| Coordinator | Same Polarity | Different polarity | Total |
|---|---|---|---|
| and | 70,296 (78%) | 19,803 (22%) | 90,099 |
| but | 1,707 (36%) | 2,981 (64%) | 4,688 |
Your task: Formulate a novel hypothesis and answer it with evidence from the database. The above and/but example illustrates what we have in mind. Note: You needn't work to predict the values in PolMatch. Take any perspective on the data that you like.