Analysis of Police Survey Data

• England and Wales Police & Crime Commissioner Elections, 2012

• Norfolk Policing Survey: "Making a Difference"

  • Quantitative & Qualitative questions
  • Crime, Anti-social behaviour, Customer Service, etc.

• Analysis of survey

  • To gain information on views of people at Norfolk
  • Information gained would assist in policy-making

Research Questions

• Is complaint, x, correlated with age, y?
• What's the difference between the means of various age groups of respondents?
• What is the frequency distribution of respondents from various districts?
• Is there a causal relationship between age, y, and the type of complaint submitted, x?
• Cluster Analysis: can respondents be grouped based on variables such as x or y?
• etc.

Research Questions

• Is x correlated with y?
• What's the difference between the means of various age groups of respondents?
• What is the frequency distribution of respondents from various districts?
• Is there a causal relationship between age and the type of complaint submitted?
• Cluster Analysis: can respondents be grouped based on their characteristic variables?
• etc.

Research Questions

• Is x correlated with y?
• What's the difference between the means of various age groups of respondents?
• What is the frequency distribution of respondents from various districts?
• Is there a causal relationship between age and the type of complaint submitted?
• Cluster Analysis: can respondents be grouped based on their characteristic variables?
• etc.

Research Questions

• Which are the most frequent/important words?
• What are the contents of the responses?
• Which responses are similar/dissimilar?
• Which words are related/unrelated?
• What sentiment is expressed in responses?
• etc.

Challenges

• Natural Language

• Synoynmy

  • Do 'theft' and 'burglary' have different meanings for our purposes?

• Polysemy

  • 'Patrol' as noun and verb

• Domain specific words

  • 'Beat' in the police terminology

• Words influenced by History/Culture

Objective: Apply operations to 'reshape' data into a format suitable for Text Mining.

• Standardisation

  • To give a unified format to all data

• Stopwords Removal

  • "That", "this", "mine", "should" are not informative
  • Domain-specific stopwords

• Thesaurus

  • 'Bobby' and 'Police'
  • 'Car parks' and 'Parking'.
  • 'Crime' and 'Offence'

Example

• Document 1:

  This is a vital issue and more bobbies must be on the beat. Increase the beat.

• Document 2:

  Police needs to solve the problem. Car parks have been taken over by bicycles! Please increase Police beat too!

• Document 3:

  I am a cyclist and I am proud of it!

Example

• Document 1:

  This is a vital issue and more bobbies must be on the beat. Increase the beat.

• Document 2:

  Police needs to solve the problem. Car parks have been taken over by bicycles! Please increase Police beat too!

• Document 3:

  I am a cyclist and I am proud of it!

Example

• Document 1:

  vital issue more police must be on beat increase beat

• Document 2:

  police needs solve problem car parks taken over by bicycle please increase police beat

• Document 3:

  bicyclist proud

• Text Preprocessing objectives:

  • To standardise textual data found in different formats
  • To remove irrelevant/less informative words
  • To replace synonymous words with a single word expressing the same meaning

• Text Preprocessing Techniques:

  • Standardisation of documents
  • Stopwords' removal
  • Thesaurus

Objective: Apply operations to generate representations of Textual data.

• Basic unit of representation
  • Terms (single words, word pairs, phrases)
  • Documents/responses
• Choose a matrix format to generate from representation units
  • Term-Document Matrix (Salton et al., 1975)
  • Term-Affiliations Matrix
• Choose settings for matrix
  • Binary Frequency
  • Term Frequency
  • Term Frequency X Inverse Document Frequency

• Term-Document Matrix
  • A representation of textual data in which Terms are represented as rows, and documents(responses) as columns.
  • Easily produced automatically.
  • Word sequences not accounted for.
  • Statistical Semantics Hypothesis (Weaver, 1955; Furnal et al., 1983)
• Binary Frequency
  • A representation that takes into account the presence/absence of Terms, by using 1s and 0s.

• Term-Document Matrix
  • A representation of textual data in which Terms are represented as rows, and documents as columns.
  • Easily produced automatically.
  • Word sequences not accounted for.
  • Statistical Semantics Hypothesis (Weaver, 1955; Furnal et al., 1983)
• Term Frequency
  • A representation that takes into account the total number of times a Term occurs in a Document.
  • Term importance defined by its frequency
  • More frequent words attain greater importance

• Term-Document Matrix
  • A representation of textual data in which Terms are represented as rows, and documents as columns.
  • Easily produced automatically.
  • Word sequences not accounted for.
  • Statistical Semantics Hypothesis (Weaver, 1955; Furnal et al., 1983)
• Term Frequency X Inverse Doc. Frequency
  • A representation that takes into account the number of times a Term occurs in documents, as well as the total number of Documents in which it occurs.
  • Term importance defined by a high Term frequency and a low document frequency
  • Rare terms in the document collection that possibly define their documents attain greatest importance

• Term-Affiliations Matrix
  • Also known as Word co-occurrences matrix
• Term Frequency

• Feature Generation objectives:
  • To generate a representation of terms (words, phrases, etc.) and responses/documents
• Representation can be of different types:
  • Term-Document Matrix
  • Term-Affiliations Matrix
  • etc.
• To assign importance to terms as required for analysis:
  • Binary
  • Term Frequency
  • Term Frequency - Inverse Document Frequency
  • etc.

• There may exist a large number of Terms in a Term Document Matrix (High-Dimensionality)

• There may exist a large number of Terms in a Term Document Matrix (High-Dimensionality)

• Not all of the Terms will be useful
  • Many Terms only present in a few documents (known as Sparse Terms)
  • Unclear how these Terms are related to other, frequently occurring ones

• There may exist a large number of Terms in a Term Document Matrix (High-Dimensionality)

• A large number of Terms poses challenges to efficient computation
  • In addition to Text Mining operations

• There may exist a large number of Terms in a Term Document Matrix (High-Dimensionality)

• Objective: Focus only on important Terms

How to measure Feature/Term Importance?

• Depends on the Text Mining operation to be applied and type of data in Term-Document Matrix

• Term Frequency lowerbounds

  • Discard all Terms with a value less than a pre-determined lowerbound

• Term Frequency X Inverse Document Frequency lowerbounds

  • Discard all Terms with a value less than a pre-determined lowerbound

• Preset Sparsity level for Terms

• etc.

• Not all terms in a Term-Document Matrix will be important
• Feature Selection Objective:
  • To distinguish important terms from unimportant terms
• Choice of Feature Selection measure:
  • Depends on the task (Classification, Clustering) and type of data in Term-Document Matrix
  • Term Frequency lowerbounds
  • Sparsity level lowerbounds
  • etc.

• Which are the most frequent/important words?
• What are the contents of the responses?
• Which responses are similar/dissimilar?
• Which words are related/unrelated?
• What sentiment is expressed in responses?
• etc.

• Which are the most frequent/important words?
• What are the contents of the responses?
• Which responses are similar/dissimilar?
• Which words are related/unrelated?
• What sentiment is expressed in responses?
• etc.

Objectives

• Organise data
• Simplify data

Objectives

• Organise data

  • Use and reveal inherent relationships
  • Derive relationships using similarity measures

• Simplify data

  • Especially for large datasets

Objectives

• Organise data

  • Use and reveal inherent relationships
  • Derive relationships using similarity measures

• Simplify data

  • Especially for large datasets

• Clustering algorithms detect relationships inherent in data by detecting statistical patterns

  • Hierarchical (Dendrograms)
  • Non-Hierarchical (Associative Word Clouds)

• Usually represent patterns by using distance measures

  • For example, Euclidean/ordinary distance
  • Other distance measures do exist

• Place data in a Euclidean space

• Clustering algorithms detect relationships inherent in data by detecting statistical patterns

  • Hierarchical (Dendrograms)
  • Non-Hierarchical (Modified Word Clouds)

• Usually represent patterns by using distance measures

  • For example, Euclidean (ordinary) distance
  • Others: Manhattan, Mahalanobis, etc.

• Place data in a Euclidean space

• Use Similarity measures to identify groups, intra-group and inter-group linkages

  • Single-Linkage, Average-Linkage, etc.
  • Cosine, Jaccuard, etc.

• Similar data tend to lie close to each other

Dendrogram

Dendrogram

• Hierarchies of groups identified
• Largest and smallest clusters identified
• Summary of data realised

  But

• Frequencies of terms not represented
• Possibly gets hard to interpret with large datasets

• Clustering Objectives:

  • Organise data
  • Identify inherent relationships/patterns in data using similarity measures
  • Use the relationships to group data

• After Clustering:

  • Data in one group is most similar to data in the same group
  • Data in one group is dissimilar from data in other groups

• Clustering Graphics:

  • Dendrograms
  • Associative Word Clouds

Research Questions

• Which are the most frequent/important words?
• What are the contents of the responses?
• Which responses are similar/dissimilar?
• Which words are related/unrelated?
• What sentiment is expressed in responses?
• etc.

Research Questions

• Which are the most frequent/important words?
• What are the contents of the responses?
• Which responses are similar/dissimilar?
• Which words are related/unrelated?
• What sentiment is expressed in responses?
• etc.

• Responses may express sentiments
• What do 'negative' responses state about policing?
• What do 'positive' responses state about policing?
• What do respondents complain about?
• What do respondents praise?
• Analyse popularity of policies
• etc.

How?

1. Supervised Approach

   • Use a classifier
   • Train it
   • Test it

2. Unsupervised Approach

   • Formulate a dictionary of 'Negative' and 'Positive' words
   • Look for the dictionary words in responses

3. Unsupervised + Heuristics Approach

   • Formulate a dictionary of 'Negative' and 'Positive' words and Grammatical rules
   • Look for dictionary words + grammatical rules in responses

• Extract a sufficient number of responses from original data
• Partition the extracted data into 'Training' and 'Testing' samples
  • \[Dataset_{Complete} (2000 responses)\]
  • \[Dataset_{Train} (1200 responses) \subset Dataset_{Complete}\]
  • \[Dataset_{Test} (800 responses) \subset Dataset_{Complete}\]
• Label the Training and Testing samples for sentiment
• Train a classifier (mathematical model) on Training samples for sentiment
• Test the trained classifier on Testing samples and report accuracy
• Deploy for use on unlabelled, new data.

Generally:

• Formulate/download a dictionary of words pre-labelled for sentiment
• Possibly formulate different dictionaries for nouns, adjectives, etc.
• Check for presence of 'Positive' or 'Negative' words in responses

• Formulate/download a dictionary of words pre-labelled for sentiment
• Possibly formulate different dictionaries for nouns, adjectives, etc
• Formulate and incorporate heuristics/rules for Grammar
• Check for presence of 'Positive' or 'Negative' words in responses
• Incorporate heuristics to make Sentiment tagging of words extensible
  • Positive/Negative Adjectives would affect Nouns
  • Adverbs would affect Verbs
  • etc.

• Sentiments may be expressed in responses

• Sentiment Analysis Objective:
  • Identify sentiments from responses
• Identification can be useful in many ways
  • Identify complaints
  • Identify praises
  • Measure popularity of policies
• Techniques for Sentiment Analysis:
  • Supervised
  • Unsupervised
  • Unsupervised + Heuristics

Utilise:

• Twitter data
  • Sometimes active
• Facebook data
  • Sometimes active
• Local newspapers' data
  • Active

Utilise:

• Twitter data
• Facebook data

• Combine demographics with Sentiment Analysis
  • Is there a relationship between Age and Sentiment?
  • Can clusters be formed on Demographics + Sentiment?
• How do Sentiments change over a period of time, x?
  • Is there a trend in the shift of Sentiments?
  • Do Sentiments change due to introduction of policies?
• Can ideological differences be identified?
• Can political affiliations be determined?
• etc.

• Utilise Twitter data
• Utilise Facebook data
• Identify types of users connected to OPCC on Twitter
• Combine with Sentiment Analysis
  • Opinion Diffusion
• etc.

• Supports basic Text Mining Operations
  • Data Pre-processing
  • Feature Generation & Weighting
  • Clustering
  • Associative Word Clouds
  • Network of Words

• Limitations
  • Memory constraints
  • Only 1 corpus per analysis
  • Approximately 2000 words/terms supported
  • 3000-4000 documents supported
  • Requires R and Java
  • Other Text Mining operations not supported

• Mr. Peter Haystead for inviting to the OPCC, Norfolk
• Dr. Beatriz De La Iglesia for lectures on Data and Text Mining
• Ramnath Vaidyanathan for Slidify
• RStudio for Shiny
• Hadley Wickham for GGPlot2
• Ingo Feirener, Kurt Hornik for Tm
• Igraph authors, for Igraph
• Drew Conway, for various tutorials