In addition to topics, phrases, words and emotions, Relative Insight also analyzes the differences and similarities in the grammar of data sets.
This article provides quick explanations of the various grammar categories analyzed in the platform.
1st person singular personal pronouns - e.g. I, me
This indicates the language of your data set is self-referential or even self-absorbed if from a personal perspective.
1st person plural personal pronouns - e.g. we, us
This indicates that the people captured within a data set present themselves as part of a unit with others.
2nd person personal pronouns - e.g. you
This indicates that the language in your dataset directly addresses others.
3rd person pronouns - e.g. she, his, they
This means that your dataset is talking about other people and is often related to politics or making judgments on the actions or behavior of others.
3rd person singular neuter personal pronoun - e.g. it
This shows that the data set is trying to disassociate itself with the subject of the text and may be trying to increase our psychological distance from the subject to make it seem more abstract.
Adjectives - e.g. old, older, oldest
This shows the data set tends to go into greater detail about the subject.
Comparative adverbs - e.g. more, less
Superlative adverbs - e.g. most, least
These categories signal that the data set is evaluating or comparing something.
Comparative after-determiner - e.g. more, less, fewer
This language is focused on quantities or amounts of something which can change or be altered, and this group might be calling for a change in this regard.
Degree adverb - e.g. very, so, too
This suggests that the data set tends to exaggerate or use hyperbole about the subject.
Interjection - e.g. oh, um, ah
This may show that the language in the set is more colloquial or informal.
Locative adverbs - e.g. there, here
This means that the language is trying to make the concept less abstract by anchoring the subject in a place or time. This reduces our psychological distance from the subject to make it seem more tangible and relatable.
Modal auxiliary - e.g. will, would, must
This shows that the data set tends to be more forward-thinking and may be looking for a change.
Numbers - e.g. 1, 1770-1827, quarter
This shows that the data set is more detail-oriented and will provide specifics instead of speculative or abstract suggestions. Can be further classified as singular cardinal numbers, hyphenated numbers, fractions or numeral numbers.
Possessive pronoun, pre-nominal - e.g. my, your, our
This shows that the data set is taking ownership of action, issue or organization, which potentially reveals a high level of engagement.
Quasi-nominal adverb of time - e.g. now, tonight, tomorrow
The language group is trying to anchor itself in a particular timeframe, perhaps stating they are socially current.
Reflexive indefinite pronoun - e.g. oneself
This shows that the data set tends to be more formal.
Superlative adjective - e.g. strongest, biggest
The group may tend to exaggerate or use hyperbole.
Unclassified word - e.g. N/A, kg/m2
This is when the data set uses words that we are not able to classify into a part of speech and may show that they are using more technical or codified terms.