Text mining and machine learning for crime classification: using unstructured narrative court documents in police academic

Figures & data

Table 1. Summary of related studies to the crime classification.

Study	Purpose	Classification	Dataset			ML algorithms
			Source	Access	Size
Thaipisutikul et al. (2021)	Illustrate the temporal and spatial occurrence of violent incident.	Burglary, Accident, Drug, Corruption and Murder.	Thai online news articles.	Open	7,879	NB LR GBM	RF KNN SVM
Carnaz et al. (2021)	Information extraction from Portuguese criminal reports.	Named entities extraction and classification using the (Who, What, Why, Where, When, and How) method.	Portuguese crime reports and criminal news.	Open	163	Perceptron
Li and Qi (2019)	Detecting serial robbery crimes committed by the same suspects.	Serial or non-serial robbery crimes.	OpenLaw website from Zhengzhou City in China.	Open	334	LR SVM KNN	NN DT
Lal et al. (2020)	Analyzing and classifying crime-related tweets.	Crime and non-crime.	Twitter.	Open	369	NB RF	DT (J48) ZeroR
Ashraf et al. (2020)	Detection of violent threat language in YouTube comments.	Violent threats and non-threats.	YouTube.	Open	28,643 sentences & 9,845 comments from 19 videos.	1D-CNN LSTM BiLSTM
Kuang et al. (2017)	Crime prevention and reduction.	226 crime types	Los Angeles Police Department	Closed	1,027,168	NMF
Percy et al. (2018)	Crime prevention and reduction.	Identify regions with similar patterns of crimes.	Dyfed-Powys Police in UK	Closed	N/A	Affinity propagation Spectral clustering
Birks et al. (2020)	Crime prevention and reduction.	Burglary	West Yorkshire Police Department	Closed	9621	LDA
Mohemad et al. (2020)	Crime prevention and reduction.	Modus operandi from Housebreaking reports: method, role, oddity, weapon, and location.	Malay Royal Police Department	Closed	100,383	k-means k-medoids
Karystianis et al. (2022)	Domestic violence surveillance and monitoring.	44 abuse types 17 Injury types	New South Wales Police Force.	Closed	492,393	N/A Text mining
Arseniev-Koehler et al. (2022)	Reduce violent death cases.	Discovering patterns about suicides, homicides, and other violent deaths.	US National Violent Death Reporting System	Closed	272,964	DATM
Victor et al. (2021)	Identify domestic violence as an active service need in child welfare investigation summaries.	Domestic violence presence or absence.	Substantiated referrals of child maltreatment in the State of Michigan.	Closed	75,809	RF
Geurts et al. (2023)	Developing a risk assessment instrument.	Repeat victimization of severe and less severe violent crimes.	Dutch police records.	Closed	116,680	DT RF
Baek et al. (2021)	Early recognition of risk situations.	21 crime types.	Korea Information System of Criminal Justice Services	Closed	5000	DNN CNN	NB SVM

ARL: Association Rule Learning; CNN: Convolutional Neural Network; DT: Decision Trees; DNN: Deep Neural Network; DATM: Discourse Atoms Topic Model; GBM: Gradient Boosting Machine; KNN: K-Nearest Neighbor; LDA: Latent Dirichlet Allocation; LR: Logistic Regression; NB: Naive Bayes; NN: Neural Network; NMF: Non-negative Matrix Factorization; RF: Random Forest; SVM: Support Vector Machines.

Table 2. Summary of studies using legal documents.

Study	Purpose	Dataset
		Source	Access	Size	Language
De Oliveira and Nascimento (2022)	Brazilian court decision document similarity detection.	The Ordinary Appeal Brought (ROI).	Open	210,000	Portuguese
Garat and Wonsever, (2022)	Protect the privacy of case participants while making court documents public.	The National Jurisprudence Base (NJB) from different courts of the Judicial Branch in Uruguay.	Open	80,000	Spanish
Nuranti et al. (2022)	Predict Indonesian court punishment category and length.	The Indo-law dataset from the Indonesian Supreme Court Decision (ISCD) website.	Open	22,630	Indonesian
Purnomo et al. (2021)	Assess Kalimantan Forest fire actors’ relationships, activities, and funding, particularly in land clearing and burning.	Forest fire cases from the official website of the Supreme Court of the Republic of Indonesia (SCRI).	Open	N/A	Indonesian
Prasad et al. (2022)	Explore decision classification of large unstructured and unannotated legal documents and formulate it as a real-life decision prediction problem.	The Indian Legal Documents Corpus (ILDC) from the Supreme Court of India.	Request	42,409	Hindi
Mandal et al. (2017)	Automate the process of similarity measurement between court case documents.	The LIIofIndia website hosted by the Legal Information Institute of India.	Open	33,545	Hindi
Gupta et al. (2023)	Use topic modeling and sentiment analysis to help users understand legal judgments and identify relevant sentiments faster.	The Supreme Court of India website.	Open	700	Hindi
Tyss et al. (2023)	Introduce article-aware classification for European Court of Human Rights (ECHR) cases to improve legal judgment prediction.	The LexGLUE ECtHR dataset from the public court database HUDOC.	Open	11,000	English
Habernal et al. (2023)	Develop methods for mining legal arguments in court decisions and automatically identifying, classifying, and analyzing them.	The CaseLaw dataset from the Harvard Law School Library and Ravel Law.	Open	65,908	English
		ECHR dataset.	Open	65,908	English
		The annotated LAM: ECHR corpus.	Open	373	English
Vatsal et al. (2023)	BERT-based methods for broad and fine-grained US Supreme Court case classification are available.	The Supreme Court Database (SCDB) from Washington University School of Law.	Open	8419	English
Song et al., (2022)	Create effective multi-label classification algorithms for legal motion detection and case prediction.	The POSTURE50K dataset from U.S. courts.	Open	50,000	English
Savelka et al., (2019)	Evaluate methods to simplify case law sentence retrieval for statutory interpretation.	The U.S. Caselaw Access Project (CAP) from federal and state courts.	Partially open Request the rest.	> 6.7 million	English
Savelka and Ashley, (2020)	Discover sentences from court documents for explaining the meaning of statutory terms.	The CAP dataset from federal and state courts.	Open	26,959 sentences	English
Sokol et al., (2020)	Analyze court case usage of common antitrust terms to determine how economics affects antitrust cases.	The CAP dataset from federal and state courts.	Partially open Request the rest.	N/A	English
Chang et al. (2020)	Solve big data caselaw research issues like data interpretation and limited caselaw extraction.	The CAP dataset from federal and state courts.	Open	9,899	English
Tang et al. (2020)	Create a semi-automatic legal knowledge graph building system and test its semantic annotation capabilities.	The CAP dataset from federal and state courts.	Open	280 legal texts	English
Castano et al. (2020)	Prove that the LATO-KM can find new legal terminology to enrich the LATO ontology.	The CAP dataset from the State of Illinois.	Open	180,000	English
Petrova et al. (2020)	Set baselines for outcome extraction and prediction.	CASELAW4 dataset subset from The CAP dataset.	Open	350,000	English

Figure 1. The proposed framework architecture.

Figure 2. Flowchart of data collection from CAP website.

Table 3. Statistics of the CAP dataset used for experimentation.

No. documents	No. words
	Longest document	Shortest document	Average
36,230	158,992	2001	7,372

Table 4. An example of the different information contained in the court document.

Content type	The narrative text from the document
	Unrequired information
Court opinion	In People v. Leach, 391 Ill. App. 3d 161, 908 N.E.2d 120 (2009), we affirmed defendant’s conviction. On September 29, 2010, pursuant to its supervisory authority, the Illinois Supreme Court directed this court to vacate our judgment in Leach, 391 Ill. App. 3d 161, 908 N.E.2d 120, and to consider whether, in light of the holding of People v. Williams, 238 Ill. 2d 125 (2010), a different result is warranted. We conclude it is not and for the following reasons affirm the judgment of the circuit court.
Offender’s life history	Defendant related that for the past two years he lived at 13795 Grace Street in Robbins with Latyonia and their three children, Emanuel, age 11, Sakiyo, age 7, and Sakera, age 6. Defendant was 30 years old. He had left Thornton High School in 1993, after his sophomore year, and supported himself and later his family doing temporary work and odd jobs.
Trial procedure	Following presentation of the State’s case, the defense moved for a finding of not guilty, asserting that the evidence failed to establish that Curtis Leach was guilty of first degree murder. At best, the defense argued, the State had proved defendant guilty of involuntary manslaughter, based upon defendant’s recklessness, or second degree murder in the heat of intense passion while engaged in mutual combat with Latyonia, who was the aggressor and outweighed the defendant by 100 pounds.
Common law text	Section 115—5(a) of the Code of Criminal Procedure of 1963 thus provides: Any writing or record, whether in the form of an entry in a book or otherwise, made as a memorandum or record of any act, transaction, occurrence, or event, shall be admissible as evidence of such act, transaction, occurrence, or event, if made in regular course of any business, and if it was the regular course of such business to make such memorandum or record at the time of such act, transaction, occurrence, or event or within a reasonable time thereafter.
	Required information
Crime scenario	According to defendant, Latyonia then ‘got to pushing me’ and he ‘pushed her back.’ They got ‘into an altercation where we was wrestling’ and Latyonia then began to hit defendant in his face, first with an open hand and then with her fist. He tried to contain Latyonia by grabbing her hands, but she was stronger and ‘had so much anger she broke loose.’ They continued wrestling on the bed until defendant managed to push Latyonia down on the bed and restrain her as she lay on her back.
Crime scene description	Evidence technician Lilliebridge processed the scene with her partner. She observed some blood on the pillow beneath Latyonia’s head as well as on the bedding underneath her hand. The bedroom was pretty neat; nothing looked out of place or thrown about. Additionally, the bed had a wooden headboard containing toiletry items which ‘were upright and undissturbed’. After processing the scene, Officer Lilliebridge later photographed defendant at the Robbins police department.
Forensic statement	His external examination revealed that Latyonia was clothed in a house shirt and underwear, and her body was accompanied by an elastic bandage. Latyonia was 5 feet 9V2 inches tall and weighed 295 pounds. Her right eye had ‘multiple petechiae hemorrhages on the upper scleral’ and ‘diffuse scleral hemorrhages’ on the lower portions; her left eye also had ‘diffuse hemorrhage’ on the lower portions. A foamy fluid had escaped from Latyonia’s nostrils and there were bluish marks on both sides of her neck. There were no wounds to Latyonia’s face or upper extremities of her body.

(Snippet from Case_id: 3716989).

Table 5. Crime dictionary (list of crime tools and associated vocabulary).

Type	Crime tool			Associated vocabulary
(a) Beating	Axe Bat Board Bottle Club Crowbar	Cup Fist Foot Hammer Hand Iron Bar	Mallet Pipe Poker Rock Stick Truncheon	Abrasion Abuse Beat Blood Blow Blunt	Bone Bruise Contusion Crush Ecchymosis Eye	Fall Fracture Head Hemorrhage Hit Jaw	Kick Laceration Lip Punch Push Shak	Skull Smash Strike Throw Torsion Trauma
(b) Shooting	Colt Firearm Gun	Handgun Pistol Revolver	Rifle Shotgun	Abdomen Ammunition Ballistic Blood Bloodstain Bullet Cartridge	Chest Defect Direction Fire Fragment Gunpowder Gunshot	Head Hemorrhage Hit Hole Injury Neck Path	Pellet Penetrate Perforate Projectile Shock Shot Slug	Soot Speed Stipple Track Trajectory Travel Wound
(c) Stabbing	Axe Blade Cleaver Fork Glass	Ice Knife Machete Pen Pick	Razor Saw Scissor Screw Sword	Abdomen Abrasion Amputation Angle Arm Attack Blood Bloodstain Bruise Chest	Chop Crush Cut Deep Depth Edge Finger Force Forearm Fracture	Hand Hemorrhage Hit Inch Incise Injury Intensity Laceration Length Mutilation	Neck Palm Penetrate Penetration Puncture Shape Sharp Size Slash Slit	Speed Stab Tear Thick Throat Track Trunk Width Windpipe Wound
(d) Strangulation	Acid Acrolein Bag Bed Blanket Cable Carbon Clothesline Cord Cyanide	Duct Gas Hose Hydrogen Ligature Monoxide Necktie Nitrogen Pillow	Plastic Scarf Sheet Sulfide Tape Tie Towel Twine Wire	Abrasion Asphyxia Asphyxiate Asphyxiation Aspirate Bind Bluish Choke	Compression Contusion Cyanotic Eardrum Engorgement Fingernail Hang	Head Knot Laceration Mark Mouth Neck Nose	Over Pressure Pull Purplish Smother Strangle Strangulation	Suffocate Suffocation Swell Swollen Tongue Vomit White

Figure 3. An example of BoW representation with TF-IDF for five documents from the CAP dataset.

Table 6. Statistics of the dataset splitting for the experiment.

Model	Class	Training	Testing	Total
CSE	Not Exist	131	56	187
	Exist	131	56	187
	Total	262	112	374
CT	Beating	22	10	32
	Shooting	45	20	65
	Stabbing	30	13	43
	Strangulation	15	6	21
	Multiclass	18	8	26
	Total	130	57	187

Table 7. The selected value of the Random State parameter ($N$) used in our experiment.

Model	DT	KNN	LR	NB	RF	SVM
CSE	254	194	20	220	163	202
CT	223	96	54	8	51	86

Figure 4. Confusion matrix for the classification model.

Figure 5. Statistics of crime documents (%) in the CAP dataset by crime tools. (a) Crime type: Beating. (b) Crime type: Shooting. (c) Crime type: Stabbing. (d) Crime type: Strangulation.

Figure 6. Heatmap of crime documents (%) in the CAP dataset. (a) Crime type: Beating. (b) Crime type: Shooting. (c) Crime type: Stabbing. (d) Crime type: Strangulation.

Figure 7. Confusion matrix of ML models using different algorithms. (a) CSE model; (b) CT model.

Table 8. Results of CSE model using different algorithms.

Algorithm	Class	Precision	Sensitivity	Specificity	F1-score	Accuracy
						Test	Validation
RF	Not Exist	88.33	94.64	87.50	91.38	91.07	90.85
	Exist	94.23	87.50	94.64	90.74
DT	Not Exist	92.00	82.14	92.86	86.79	87.50	87.39
	Exist	83.87	92.86	82.14	88.14
LR	Not Exist	90.38	83.93	91.07	87.04	87.50	87.01
	Exist	85.00	91.07	83.93	87.93
NB	Not Exist	81.54	94.64	78.57	87.60	86.61	86.64
	Exist	93.62	78.57	94.64	85.44
SVM	Not Exist	84.75	89.29	83.93	86.96	86.61	86.31
	Exist	88.68	83.93	89.29	86.24
KNN	Not Exist	84.91	80.36	85.71	82.57	83.04	82.46
	Exist	81.36	85.71	80.36	83.48

Table 9. Comparing result of CSE model with previous study.

Study	Labels	Database	Size	Algorithm	Accuracy
Lal et al. (2020)	Crime and non-crime	Twitter	500 tweets	TF-IDF + RF	98.10
Our CSE model	Not Exist and Exist	The CAP	374 documents	TF-IDF + RF	91.07

Table 10. Results of CT model using different algorithms.

Algorithm	Class	Precision	Sensitivity	Specificity	F1-score	Accuracy
						Test	Validation
SVM	Beating	72.73	80.00	93.62	76.19	82.46	82.31
	Shooting	90.48	95.00	94.59	92.68
	Stabbing	80.00	92.31	93.18	85.71
	Strangulation	100	83.33	100	90.91
	Multiclass	60.00	37.50	95.92	46.15
LR	Beating	80.00	80.00	95.74	80.00	80.70	80.77
	Shooting	86.96	100	91.89	93.02
	Stabbing	72.22	100	88.64	83.87
	Strangulation	100	66.67	100	80.00
	Multiclass	50.00	12.50	97.96	20.00
RF	Beating	80.00	80.00	95.74	80.00	80.70	80.77
	Shooting	95.00	95.00	97.30	95.00
	Stabbing	65.00	100	84.09	78.79
	Strangulation	100	83.33	100	90.91
	Multiclass	50.00	12.50	97.96	20.00
KNN	Beating	75.00	60.00	95.74	66.67	75.44	75.38
	Shooting	90.91	100	94.59	95.24
	Stabbing	55.00	84.62	79.54	66.67
	Strangulation	100	33.33	100	50.00
	Multiclass	80.00	50.00	97.96	61.54
DT	Beating	70.00	70.00	93.62	70.00	70.18	70.77
	Shooting	82.61	95.00	89.19	88.37
	Stabbing	81.82	69.23	95.45	75.00
	Strangulation	57.14	66.67	94.12	61.54
	Multiclass	16.67	12.50	89.80	14.29
NB	Beating	64.29	90.00	89.36	75.00	66.67	66.92
	Shooting	94.12	80.00	97.30	86.49
	Stabbing	50.00	53.85	84.09	51.85
	Strangulation	100	50.00	100	66.67
	Multiclass	33.33	37.50	87.76	35.29

Table 11. Comparing result of CT model with previous studies.

Study	Labels	Database	Size	Algorithm	Accuracy
Petrova et al. (2020)	Affirm, Reverse, and Mixed	The CAP	500 documents	LSTM	81.00
Thaipisutikul et al. (2021)	Burglary, Drug, Murder, Accident, and Corruption.	Thailand Online News Articles	7,879 articles	TF-IDF + SVM	79.41
Our CT model	Beating, Shooting, Stabbing, Strangulation, and Multilabel	The CAP	374 documents	TF-IDF + SVM	82.46

Figure 8. Classification of documents from the CAP dataset according to our experience. (a) Crime scene existence model. (b) Crime type model.

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Availability of data and materials

The dataset supporting the conclusions of this article is available in the [Harvard Law School Collection] repository, [https://case.law/about/].