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| Metric | Information |
|---|---|
| DESCRIPTIVE | |
| Number of words (total) | The total number of words in the text without punctuation marks. |
| Number of distinct words (total) | The total number of distinct lower words in the text without punctuation marks, without digits, without spaces, (only words with alphabetic characters) |
| Number of distinct words (incidence per 1000 words) | The number of distinct words in the text per 1000 words |
| Number of words with punctuation (total) | The total number of words in the text with punctuation marks. |
| Number of paragraphs (total) | The total number of paragraphs in the text (Paragraphs are defined by hard returns within the text). |
| Number of paragraphs (incidence per 1000 words) | The number of paragraphs in the text per 1000 words. |
| Number of sentences (total) | The total number of sentences in the text. |
| Number of sentences (incidence per 1000 words) | The number of sentences in the text per 1000 words. |
| Length of paragraphs (mean) | The average number of sentences in each paragraph within the text |
| Standard deviation of length of paragraphs | The standard deviation of the measure for the mean length of paragraphs within the text. |
| Number of words (length) in sentences (mean) | The average number of words in each sentence within the text. |
| Number of words (length) in sentences (standard deviation) | The standard deviation of the measure for the mean length of sentences within the text. |
| Number of words (length) of sentences without stopwords (mean) | The average number of words in each sentence within the text without stop words (NLTK stop words: https://pythonspot.com/nltk-stop-words/https://pythonspot.com/nltk-stop-words/ ) |
| Number of words (length) of sentences without stopwords (standard deviation) | The standard deviation of the measure for the mean length of sentences within the text without stop words. |
| Mean number of syllables (length) in words | The average number of syllables in words. We are using cmudict (a pronouncing dictionary for north american english words) to separate words into syllables. |
| Standard deviation of the mean number of syllables in words | The standard deviation of the measure for the mean number of syllables in the words within the text. |
| Mean number of letters (length) in words | The average number of letters in words |
| Standard deviation of the mean number of letters in words | The standard deviation of the mean number of letters in words |
| Mean number of letters (length) in words without stopwords | The average number of letters in words without stop words |
| Standard deviation of the mean number of letters in words without stopwords | The standard deviation of the mean number of letters in words without stop words |
| Mean number of letters (length) in lemmas | The average number of letters in lemmas |
| Standard deviation of the mean number of letters in lemmas | The standard deviation of the mean number of letters in lemmas |
| LEXICAL DIVERSITY | |
| Lexical Density | Lexical Density is the ratio obtained by dividing the types (the total number of content words) occurring in a text by the total number of words.Ratio: Content/(Content+Functional) |
| Noun Density | Noun Density is the ratio obtained by dividing the types (the total number of nouns) occurring in a text by the total number of words. |
| Verb Density | Verb Density is the ratio obtained by dividing the types (the total number of verbs) occurring in a text by the total number of words. |
| Adjective Density | Adjective Density is the ratio obtained by dividing the types (the total number of adjectives) occurring in a text by the total number of words. |
| Adverb Density | Adverb Density is the ratio obtained by dividing the types (the total number of adverbs) occurring in a text by the total number of words. |
| STTR (Simple Type-Token Ratio) | STTR is the ratio obtained by dividing the types (the total number of different words) occurring in a text by its tokens (the total number of words) (1957, Templin) |
| CTTR (Content Type-Token Ratio) | CTTR is the ratio obtained by dividing the types (the total number of different content or lexical words) occurring in a text by its tokens (the total number of content words). |
| NTTR (Noun Type-Token Ratio) | NTTR is the ratio obtained by dividing the types (the total number of different nouns) occurring in a text by its tokens (the total number of nouns). |
| VTTR (Verb Type-Token Ratio) | VTTR is the ratio obtained by dividing the types (the total number of different verbs) occurring in a text by its tokens (the total number of verbs). |
| AdjTTR (Adj Type-Token Ratio) | AdjTTR is the ratio obtained by dividing the types (the total number of different adjectives) occurring in a text by its tokens (the total number of adjectives). |
| AdvTTR (Adv Type-Token Ratio) | AdvTTR is the ratio obtained by dividing the types (the total number of different adverbs) occurring in a text by its tokens (the total number of adverbs). |
| LSTTR (Lemma Simple Type-Token Ratio) | LSTTR is the ratio obtained by dividing the types (the total number of different lemmas) occurring in a text by its tokens (the total number of words). |
| LCTTR (Lemma Content Type-Token Ratio) | LCTTR is the ratio obtained by dividing the types (the total number of different content lemmas) occurring in a text by its tokens (the total number of content words). |
| LNTTR (Lemma Noun Type-Token Ratio) | LNTTR is the ratio obtained by dividing the types (the total number of different lemmas of noun category) occurring in a text by its tokens (the total number of nouns). |
| LVTTR (Lemma Verb Type-Token Ratio) | LVTTR is the ratio obtained by dividing the types (the total number of different lemmas of verb category) occurring in a text by its tokens (the total number of verbs). |
| LAdjTTR (Lemma Adj Type-Token Ratio) | LAdjTTR is the ratio obtained by dividing the types (the total number of different lemmas of adjective category) occurring in a text by its tokens (the total number of adjectives). |
| LAdvTTR (Lemma Adv Type-Token Ratio) | LAdvTTR is the ratio obtained by dividing the types (the total number of different lemmas of adverb category) occurring in a text by its tokens (the total number of adverbs). |
| Honoré Lexical Density | Honore = (100 * log N)/(1- ( V1 / V)) ; N is the total number of words in the text, V is the total number of different words in the text and V1 is the hapax legomena (Honoré, 1979) |
| Maas Lexical Density | Maas = (log N – log V) / (log² V) ;N is the total number of words in the text and V is the total number of different words in the text (Maas, 1966) |
| Measure of Textual Lexical Diversity (MTLD) | MTLD (measure of textual lexical diversity) (2005, McCarthy). |
| READABILITY ABILITY | |
| Flesch-Kincaid Grade level | Flesch-Kincaid grade level (Flesch,1948), tells you the American school grade you would need to be in to comprehend the material on the page. Flesch-Kincaid grade level formula = 0.39 x (words/sentences) + 11.8 x (syllables/words) - 15.59. |
| Simple Measure Of Gobbledygook (SMOG) grade | Simple Measure Of Gobbledygook (SMOG) grade (McLaughlin, 1969) SMOG formula is 1.0430 * SQRT (30*totalcomplex / totalsentences) + 3.1291. Totalcomplex are all number of words of more than two syllables in totalsentences (at least 30). |
| WORD FREQUENCY | |
| The minimum word frequency per sentence | Average of word frequency of the lowest word frequency word in each sentence |
| Number of rare nouns | The number of rare nouns. A rare noun is a noun whose word frequency value is less than or equal to 4 logarithm value. |
| Number of rare nouns (incidence per 1000 words) | The number of rare nouns (whose word frequency value is less than or equal to 4 logarithm value) in the text per 1000 words |
| Number of rare adjectives | The number of rare adjectives. A rare adjective is an adjective noun whose word frequency value is less than or equal to 4 logarithm value. |
| Number of rare adjectives (incidence per 1000 words) | The number of rare adjectives (whose word frequency value is less than or equal to 4 logarithm value) in the text per 1000 words |
| Number of rare verbs | The number of rare verbs. A rare verb is a verb whose word frequency value is less than or equal to 4 logarithm value. |
| Number of rare verbs (incidence per 1000 words) | The number of rare verbs (whose word frequency value is less than or equal to 4 logarithm value) in the text per 1000 words |
| Number of rare adverbs | The number of rare adverbs. A rare adverb is an adverb whose word frequency value is less than or equal to 4 logarithm value. |
| Number of rare adverbs (incidence per 1000 words) | The number of rare adverbs (whose word frequency value is less than or equal to 4 logarithm value) in the text per 1000 words |
| Number of rare content words | The number of rare content words. Rare content words are nouns, verbs, adjectives and adverbs whose word frequency value is less than 4 |
| Number of rare content words (incidence per 1000 words) | The number of rare content words (whose word frequency value is less than 4) in the text per 1000 words |
| Number of distinct rare content words | The number of distinct rare content words. Rare content words are nouns, verbs, adjectives and adverbs whose word frequency value is less than 4 |
| Number of distinct rare content words (incidence per 1000 words): | The number of distinct rare content words (whose word frequency value is less than 4) in the text per 1000 words |
| Mean of rare lexical words | The average of rare lexical words (whose word frequency value is less than 4) with respect to the total of lexical words |
| Mean of distinct rare lexical words | The average of distinct rare lexical words (whose word frequency value is less than 4) with respect to the total of distinct lexical words |
| VOCABULARY KNOWLEDGE | |
| number of A1 vocabulary in the text | The number of A1 vocabulary in the text |
| incidence score of A1 vocabulary (per 1000 words) | The number of A1 vocabulary in the text per 1000 words |
| number of A2 vocabulary in the text | The number of A2 vocabulary in the text |
| incidence score of A2 vocabulary (per 1000 words) | The number of A2 vocabulary in the text per 1000 words |
| number of B1 vocabulary in the text | The number of B1 vocabulary in the text |
| incidence score of B1 vocabulary (per 1000 words) | The number of B1 vocabulary in the text per 1000 words |
| number of B2 vocabulary in the text | The number of B2 vocabulary in the text |
| incidence score of B2 vocabulary (per 1000 words) | The number of B2 vocabulary in the text per 1000 words |
| number of C1 vocabulary in the text | The number of C1 vocabulary in the text |
| incidence score of C1 vocabulary (per 1000 words) | The number of C1 vocabulary in the text per 1000 words |
| Number of content words not in A1-C1 vocabulary | The number of content words not in A1-C1 vocabulary in the text |
| Incidence score of content words not in A1-C1 vocabulary (per 1000 words) | The number of content words not in A1-C1 vocabulary in the text per 1000 words |
| WORD INFORMATION | |
| Number of content words | The number of content words |
| Number of content words (incidence per 1000 words) | The number of content words in the text per 1000 words |
| Number of nouns | The number of nouns |
| Number of nouns (incidence per 1000 words) | The number of nouns in the text per 1000 words |
| Number of proper nouns | The number of proper nouns |
| Number of proper nouns (incidence per 1000 words) | The number of proper nouns in the text per 1000 words |
| Ratio of proper nouns for all nouns (proper and common nouns) | The ratio of proper nouns for all nouns (proper and common nouns) |
| Number of adjectives | The number of adjectives |
| Number of adjectives (incidence per 1000 words) | The number of adjectives in the text per 1000 words |
| Number of adverbs | The number of adverbs |
| Number of adverbs (incidence per 1000 words) | The number of adverbs in the text per 1000 words |
| Number of verbs | The number of verbs |
| Number of verbs (incidence per 1000 words) | The number of verbs in the text per 1000 words |
| Number of verbs in past tense | The number of verbs in past tense in the text |
| Number of verbs in past tense (incidence per 1000 words) | The number of verbs in past tense per 1000 words |
| Number of verbs in present tense | The number of verbs in present tense in the text |
| Number of verbs in present tense (incidence per 1000 words) | The number of verbs in present tense per 1000 words |
| Number of verbs in future tense | The number of verbs in future tense in the text |
| Number of verbs in future tense (incidence per 1000 words) | The number of verbs in future tense per 1000 words |
| Number of verbs in indicative mood | The number of verbs in indicative mood in the text |
| Number of verbs in indicative mood (incidence per 1000 words) | The number of verbs in indicative mood per 1000 words |
| Number of verbs in imperative mood | The number of verbs in imperative mood in the text |
| Number of verbs in imperative mood (incidence per 1000 words) | The number of verbs in imperative mood per 1000 words |
| Number of irregular verbs in past tense | The number of irregular verbs in past tense |
| Number of irregular verbs in past tense (incidence per 1000 words) | The number of irregular verbs in past tense per 1000 words |
| Mean of irregular verbs in past tense in relation to the number of verbs in past tense | Mean of the number of irregular verbs in past / number of verbs in past |
| number of personal pronouns | The number of pronouns |
| incidence score of pronouns (per 1000 words) | Number of pronouns per 1000 words |
| Number of pronouns in first person | The number of first-person pronouns |
| Incidence score of pronouns in first person (per 1000 words) | The number of first-person pronouns per 1000 words |
| Number of pronouns in singular first person | The number of first-person singular pronouns |
| Incidence score of pronouns in singular first person (per 1000 words) | The number of first-person singular pronouns per 1000 words |
| Number of pronouns, third person | The number of third-person pronouns |
| incidence score of pronouns, third person (per 1000 words) | The number of third-person pronouns per 1000 words |
| SYNTACTIC COMPLEXITY | |
| Left embeddedness (Mean of number of words before the main verb) | The mean of the number of words before the main verb of the main clause in sentences |
| Number of decendents per noun phrase (mean) | The mean of the number of decendents per noun phrase |
| Number of modifiers per noun phrase (mean) | The mean of the number of modifiers per noun phrase |
| Mean of the number of levels of dependency tree(Depth) | The average of the number of levels of dependency tree per sentence |
| Number of subordinate clauses | The number of subordinate clauses in the text |
| Number of subordinate clauses (incidence per 1000 words) | The number of subordinate clauses in the text per 1000 words |
| Number of relative subordinate clauses | The number of relative subordinate clauses in the text |
| Number of relative subordinate clauses (incidence per 1000 words) | The number of relative subordinate clauses in the text per 1000 words |
| Mean of punctuation marks per sentence | The average of punctuation marks per sentence |
| Number of propositions | Number of propositions |
| Mean of the number of propositions per sentence | The average of the number of propositions per sentence |
| Mean of the number of VPs per sentence | The average of the number of VPs per sentence |
| Mean of the number of NPs per sentence | The average of the number of NPs per sentence |
| Noun phrase density, incidence | The number of noun phrases in the text per 1000 words |
| Verb phrase density, incidence | The number of verb phrases in the text per 1000 words |
| Number of passive voice verbs | The number of verbs in passive voice in the text |
| Number of passive voice verbs (incidence per 1000 words) | The number of verbs in passive voice per 1000 words |
| Mean of passive voice verbs | Mean of passive voice verbs |
| Number of agentless passive voice verbs | The number of agentless passive voice verbs in the text |
| Agentless passive voice incidence | The number of verbs in passive voice per 1000 words |
| Number of negative words | The number of words with lemma "not" |
| Negation incidence | The number of words with lemma "not" per 1000 words |
| Number of verbs in gerund form | The number of verbs in gerund form in the text |
| Gerund incidence | The number of verbs in gerund form per 1000 words |
| Number of verbs in infinitive form | The number of verbs in infinitive form in the text |
| Infinitive incidence | The number of verbs in infinitive form per 1000 words |
| WORD SEMANTIC INFORMATION | |
| Mean values of polysemy in the WordNet lexicon | The average of the polysemy values of nouns and verbs in the text that have entries in the WordNet lexicon |
| Mean hypernym values of verbs in the WordNet lexicon | The average of the hypernym values of verbs in the text that have entries in the WordNet lexicon |
| Mean hypernym values of nouns in the WordNet lexicon | The average of the hypernym values of nouns in the text that have entries in the WordNet lexicon |
| Mean hypernym values of nouns and verbs in the WordNet lexicon | The average of the hypernym values of nouns and verbs in the text that have entries in the WordNet lexicon |
| REFERENTIAL COHESION | |
| Noun overlap, adjacent sentences, binary, mean | Noun overlap measure is binary (there either is or is not any overlap between a pair of adjacent sentences in a text ). Noun overlap measures the proportion of sentences in a text for which there are overlapping nouns,in the sense that the noun must match exactly. |
| Noun overlap, all of the sentences in a paragraph or text, binary, mean | Global Noun overlap measures which is the average overlap between all pairs of sentences in the text for which there are overlapping nouns, in the sense that the noun must match exactly. |
| Argument overlap, adjacent sentences, binary, mean(CRFAOI) | Argument overlap measure is binary (there either is or is not any overlap between a pair of adjacent sentences in a text ). Argument overlap measures the proportion of sentences in a text for which there are overlapping the between nouns (in singular or plural form=lemma, e.g., “table”/”tables”) and personal pronouns (“he”/”he”) |
| Argument overlap, all of the sentences in a paragraph or text, binary, mean | Global Argument overlap measures which is the average overlap between all pairs of sentences in the text for which there are overlapping stem nouns and personal pronouns. |
| Stem overlap, adjacent sentences, binary, mean | Stem overlap measure is binary (there either is or is not any overlap between a pair of adjacent sentences in a text ). Stem overlap measures the proportion of sentences in a text for which there are overlapping between a noun in one sentence and a content word (i.e., nouns,verbs, adjectives, adverbs) in a previous sentence that shares a common lemma (e.g., “tree”/”treed”;”mouse”/”mousey”). |
| Stem overlap, all of the sentences in a paragraph or text, binary, mean | Global Stem overlap measures which is the average overlap between all pairs of sentences in the text for which there are overlapping. Between a noun in one sentence and a content word (i.e., nouns,verbs, adjectives, adverbs) in a previous sentence that shares a common lemma (e.g., “tree”/”treed”;”mouse”/”mousey”). |
| Content word overlap, adjacent sentences, proporcional, mean | Content word overlap adjacent sentences proporcional mean refers to the proportion of content words (nouns, verbs,adverbs,adjectives, pronouns) that shared between pairs of sentences.For example, if a sentence pair has fewer words and two words overlap, The proportion is greater than if a pair has many words and two words overlap. This measure may be particulaly useful when the lenghts of the sentences in the text are principal concern. |
| Content word overlap, adjacent sentences, proporcional, standard deviation | The standard deviation of the measure for the mean content word overlap of sentences within the text. |
| Content word overlap, all of the sentences in a paragraph or text, proporcional, mean | Content word overlap adjacent sentences proporcional mean refers to the proportion of content words (nouns, verbs,adverbs,adjectives, pronouns) that shared between pairs of sentences.For example, if a sentence pair has fewer words and two words overlap, The proportion is greater than if a pair has many words and two words overlap. This measure may be particulaly useful when the lenghts of the sentences in the text are principal concern. |
| Content word overlap, all of the sentences in a paragraph or text, standard deviation | The standard deviation of the measure for the mean content word overlap of sentences within the text. |
| SEMANTIC OVERLAP | |
| Semantic Similarity between adjacent sentences (mean) | This index computes mean semantic similarity cosines for adjacent sentences. This measures how conceptually similar each sentence is to the next sentence. |
| Semantic Similarity between all possible pairs of sentences in a paragraph (mean) | This index computes mean semantic similarity (cosines). However, for this index all sentence combinations are considered, not just adjacent sentences. This computes how conceptually similar each sentence is to every other sentence in the text. |
| Semantic Similarity between adjacent paragraps (mean) | This index computes the mean of the semantic similarity (cosines) between adjacent paragraphs. |
| Semantic Similarity between adjacent sentences (standard deviation) | This index computes standard deviation of semantic similarity (cosines) for adjacent sentences. This measures how consistent adjacent sentences are overlaped semantically. |
| Semantic Similarity between all possible pairs of sentences in a paragraph (standard deviation) | This index computes the standard deviation of semantic similarity (cosine) of all sentence pairs within paragraphs. |
| Semantic Similarity between adjacent paragraps (standard deviation) | This index is the standard deviation of semantic similarity (cosines) between adjacent paragraphs. |
| CONNECTIVES | |
| Number of all connectives | The total number of connectives. |
| Number of all connectives (incidence per 1000 words) | The total number of connectives per 1000 words. |
| Causal connectives | The number of causal connectives. |
| Causal connectives (incidence per 1000 words) | The number of causal connectives per 1000 words. |
| Logical connectives | The number of logical connectives. |
| Logical connectives (incidence per 1000 words) | The number of logical connectives per 1000 words. |
| Adversative/contrastive connectives | The number of adversative connectives. |
| Adversative/contrastive connectives (incidence per 1000 words) | The number of adversative connectives per 1000 words. |
| Temporal connectives | The number of temporal connectives. |
| Temporal connectives (incidence per 1000 words) | The number of temporal connectives per 1000 words. |
| Conditional connectives | The number of conditional connectives. |
| Conditional connectives (incidence per 1000 words) | The number of conditional connectives per 1000 words. |
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