This document introduces computational stylistics as a sub-discipline of computational linguistics that studies patterns in linguistic encoding of information using computers. It evolved in the 1960s and allows for more accurate analysis of stylistic patterns through quantification of elements like word types, lengths, and sentences. Computational stylistics can be used to analyze literary works and distinguish authors' unique styles. It analyzes fields like poetry, novels and plays. Corpora include works from Shakespeare and Emily Dickinson. The analysis helps determine an author's grammar, lexicon, semantics and phonology. One study accurately classified texts into categories like cooking, ecology and music using easily computed variables like punctuation and word frequencies.

1. COMPUTATIONAL
STYLISTICS
NOOR KHAIRIN NAWWARAH KHALID 0822002
NUR HUSNA AYUNI ABDULLAH 0820824
NUR SYAIRA AZMI 0825436
NUR IZZATI IDRIS 0829136

2. INTRODUCTION
 a sub-discipline of computational linguistics.
 It evolved in the 1960s
 the study of patterns formed in the process of the linguistic
encoding of information.
 In the area of “stylometry,” the study of linguistic style, which
the computer is used to generate data on the types, number and
length of words and sentences.

3.  Through the use of computers, it should be possible to achieve
more accurate detection and explanation of such linguistic
patterns
 Stylistic analysis is also integral to the detection of uses of
language which distinguish one author from another. An
author's style is his signature.
 Through analysis of individual style, researchers can find clues
to unique characteristics in linguistic pattern.

4. THE SCOPE
 Literary fields; play, poems, novel, short stories etc.
 machine translation

5. CORPUS
 Anything that are related to literary works that will be
chosen.
 Examples Shakespeare (Romeo and Juliet), Emily
Dickinson’s poems, etc.

6. RELEVANCE OR APPLICATION TO
LANGUAGE RESEARCH
 To determine the style of literary works; grammar,
lexis, semantics, phonological properties.
 To identify linguistic features; Arbitrariness, creativity' or
'open-endedness‘.

7. STUDY ON COMPUTATIONAL
STYLISTICS
 On Building An Automatic Text Classification Model
With Minimal Computational Costs
 www.um.es/lacell/aesla/contenido/pdf/6/ mtnez4.pdf
 By Nerea Martínez Aroca from Universidad de Murcia
 Purpose :
 - to try and design a model of automatic text
classification which allows text category discrimination
as a prior step to new case assignment to previously
established text categories on the basis of a series of
linguistic and easily computable parameters and thus,
reduced computational costs.

8.  Corpus:
- Cooking recipes, Ecology, Music, Oncology, Physics and
Religion.
 Methodology:
- (corpus) 4 written texts were collected from different
websites.
- ( variables) Punctuation variables, Lexical Distribution
variables and Most frequent words of the BNC1

9. FINDINGS
 the set of linguistic variables proposed, in addition to
being easily identified and computed -in contrast to other
linguistic features used in research studies in the area
that require manual supervision because of ambiguity
can accurately discriminate among text categories as
seen through CA ( cluster analysis)
 DA(Discriminant Function Analysis) offered a 100%
accurate classification of text samples into the categories
analysed.
 all groups of variables achieved a 100% accuracy rate
with no cases misclassified.

10. REFLECTION
 Time constraints
 Not much study were found under this topic.
 Even when we found a study, we could not
comprehend the whole study. Because it involves
with figures/ statistics.