An Automated Approach to Mining and Visual Analytics of

Spatiotemporal Context from Online Media Articles

Bolelang Sibolla, Laing Lourens, Retief Lubbe and Mpheng Magome

Meraka Intsitute, Council for Scientific and Industrial Research, Meiring Naude Road, Brummeria, Pretoria, South Africa

Keywords: Text Classification, Location Extraction, Geospatial Visual Analytics, Machine Learning, Spatio-Temporal

Events.

Abstract: Traditionally spatio-temporally referenced event data was made available to geospatial applications through

structured data sources, including remote sensing, in-situ and ex-situ sensor observations. More recently,

with a growing appreciation of social media, web based news media and location based services, it is an

increasing trend that geo spatio-temporal context is being extracted from unstructured text or video data

sources. Analysts, on observation of a spatio-temporal phenomenon from these data sources, need to

understand, timeously, the event that is happening; its location and temporal existence, as well as finding

other related events, in order to successfully characterise the event. A holistic approach involves finding the

relevant information to the phenomena of interest and presenting it to the analyst in a way that can

effectively answer the “what, where, when and why” of a spatio-temporal event. This paper presents a data

mining based approach to automated extraction and classification of spatiotemporal context from online

media publications, and a visual analytics method for providing insights from unstructured web based media

documents. The results of the automated processing chain, which includes extraction and classification of

text data, show that the process can be automated successfully once significantly large data has been

accumulated.

1 INTRODUCTION

In the current age of the digital earth and big data,

apart from the fact that more sensors that provide

data are available, there has been a concurrent

proliferation of unstructured geospatial data content.

This data is available as web based formal media

documents or discussions on social media networks.

In general people have become more location and

time aware. Similarly analysts have become

interested in supplementing structured geospatial

data from remote sensing satellite sources and in-

situ/ ex-situ sensor observation networks with data

acquired from media. This has become a valuable

source of information as it provides a socioeconomic

perspective, which answers the following questions:

What is happening, where is it happening, when is it

happening and why is it happening?

The last question can be answered in two phases.

Firstly, by extraction of the context from the media

document; secondly, from an analysis of time based

trends and correlation with other phenomena that

occur within the same or overlapping temporal

extent.

In most cases this information is required as it

happens, whereas it is also important to perform

analysis on historic data in order to observe trends,

which can be used for preparedness going forward.

Visual Analytics plays a vital role in decision

support as it provides the user or stakeholder with

various tools for interrogating the data.

In order to provide this kind of decision support,

it is necessary while retrieving data from the web, to

ensure that it fits the topic description, to classify the

data such that it fits the expected analysis, and to

geocode the locations of interest in order to correlate

with other map based activities. This is an involved

process which has to be executed seamlessly and

automatically to ensure swift delivery of the desired

information.

The objective of this paper is thus to provide a

mechanism for automated, seamless extraction of

geo spatiotemporal contextual information from

unstructured web based news media publications.

The second objective is to provide a visual analytics

based solution to enhance and reveal structural

Sibolla, B., Lourens, L., Lubbe, R. and Magome, M.

An Automated Approach to Mining and Visual Analytics of Spatiotemporal Context from Online Media Articles.

DOI: 10.5220/0006699602110222

In Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2018), pages 211-222

ISBN: 978-989-758-294-3

211

patterns, in order to support a geographic

information, web based decision support application.

2 BACKGROUND AND RELATED

WORK

Context in information science is defined as “any

information that can be used to characterize or

improve the interpretation of an entity, which could

be a person, place or object” (Dey 2001; Robertson

and Horrocks, 2017). Zimmerman et al (2007)

review a number of context definitions with the

purpose of extending these definitions. Following

both Dey (2001) and Zimmerman et al (2007),

context can be described by five categories, namely:

individuality, activity, location, time, and relations.

This paper focuses on the location and time aspect to

define spatio-temporal context.

Substantial work has been done in the field of

knowledge discovery from unstructured text

documents. Tan (1999) provides a review of text

mining and aligns existing methods on whether they

serve the function of text refinement which

transform text into an intermediate format that can

then be ingested for further processing in order to

derive knowledge, or knowledge distillation that

deduces knowledge from text. In order to make

sense of text data, it is usually the case that the data

has to be classified into categories that can be further

analysed. Feldman and Sanger (2007) discuss

various existing algorithms and techniques for text

categorisation, clustering and information extraction.

In addition to classification, when deriving

spatiotemporal context from text documents, it is

important to derive temporal knowledge on the

occurrence of events. Mei and Zhai (2005) discuss

discovering temporal patterns from text with a time

stamp. More recent applications of deriving spatio-

temporal context from unstructured text are

presented by Mirrezaei et al (2016) and Berenbaum

et al (2016).

More directed to this research is geographic

information retrieval research which involves

extracting information about location from text

documents and ranking their relevance to the topic.

It also involves identifying place names within text

from publications, assessing and addressing

ambiguity in identified place names, ranking the

articles and grouping them based on thematic or

spatial similarities and developing user interfaces

that aid users in information discovery(Jones and

Purves, 2008; Robertson and Horrocks, 2017).

2.1 Related Work

This section discusses related work in the field of

text extraction from text documents. It is restricted

to methods that explicitly consider spatio-temporal

events and their chronological appearance and

relations. Extraction of locations from text as well as

methods for classification of articles by relevance to

topic are discussed.

2.1.1 Approaches to Spatio-Temporal

Context Extraction from Text

Generally in order to extract identified entities of

interest, such as locations, events, organisations or

people, from the text, a Named Entity Recognition

(NER) method is employed (Strotgen et al, 2010). A

description of NER and its origins in the context of

Natural Language Processing for the purpose of

deriving named entities from text is provided by

Nadeau and Sekine (2007). Chasin et al (2014),

provide a comprehensive review of the NER

methods of extracting words and phrases that

describe place names and locations from documents.

Chasin et al’s method, starts with extraction of

significant events from text, then they extract

temporal relations amongst the identified events.

This is followed by identifying and extracting named

entities in the text relating to the events of interest

also known as topics. The locations are then

geocoded to get the coordinates needed for

displaying the point on a map. They provide a map

display with timelines to show the chronological

occurrence of these events. Other methods for fusion

of spatial and temporal context extraction include

Strotgen et al (2010) and Robertson and Horrocks

(2017).

2.1.2 Location Extraction from Text

This section gives a brief review of NER methods

that have been used specifically for the extraction of

locations and place names from text. Lingad et al

(2013) compared the effectiveness of four different

Named Entity Recognition tools for extracting

locations using a dataset of 3200 tweets related to

natural disasters. They found that the Stanford parser

had the best out-of-the-box performance compared

to OpenNLP, TwitterNLP and Yahoo! Placemaker.

Gehring (2015) goes into great detail about the

challenges of location ambiguities as well as context

ambiguities. To address location ambiguity Gehring

proposes using the relationships between the

locations in a gazetteer to form a hierarchy, at the

top of which would be the most probable location.

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This method would also address the issue of

locations that are often confused with people’s

names by NER tools, according to Gehring. Worth

noting is also the survey results of Gehring’s thesis

which show that people find a map accompanying a

news article to be vital to understanding the location

context.

In addition to the process of identifying location

context from articles, it is also important to identify

the topic which the document is reporting about. In

order to answer the question related to what is

happening and where it is happening. In Natural

Language processing, this is done primarily through

the process of topic modelling. Topic modelling is a

form of clustering and classification of documents

based on the concepts and topics embedded in its

text. Most notable methods for topic modelling

include: Latent Semantic Latent Semantic Indexing

(LSI), Mixture of unigrams model, Probabilistic

Latent Semantic Indexing (pLSI), Latent Dirichlet

Allocation (LDA) (Uys et al, 2008).

2.1.3 Relevance of Text to Topic by

Classification

Two major types of classifications are found in

literature namely binary classification and multiclass

classification. Binary classification divides items

into two groups and determines which group each of

the items is most suitable for. In this application

multi-class classification is discussed in relation to

topic modelling.

Binary Classification

When articles are first received it is necessary to

eliminate those that are not relevant to the subject

matter. This is done by using a binary classification

method with two classes relevant articles and

irrelevant. Well known machine learning methods

for binary classification specifically for text

documents include Simple Bayesian classifiers,

Decision trees, Neural Networks (McCallum and

Nigam, 1998; Li and Jain, 1998) and Support Vector

Machines which have been found to be the most

effective (Joachims, 1998) and are therefore used in

deriving the methodology for this study. The SVM

method, assigns newly acquired and pre-processed

articles a category based on a model that is built

from analysing a training set with data that has been

labeled to belong to the two categories.

Tong and Koller (2001), Manevitz and Yousef,

(2002) address the suitability of a one-class Support

Vector Machine (SVM) for the process of binary

classification. Manevitz and Yousef (2002) and

Tong and Koller (2001) made use of the term

frequency- inverse document frequency (tf-idf)

feature selection metric in their experiments and

found that the One-Class Support Vector Machine as

proposed by Scholkopf et al (1999) worked best

overall for document classification. They also found

that it was the more robust algorithm with respect to

classifying smaller categories.

Kubat and Matwin (1997), Liu et al (2003) and

Wang and Japkowicz (2010) all deal with inherent

class imbalance that arises when trying to find a few

articles of interest among all the articles on the web.

Kubat and Matwin (1997) in particular show why

the use of decision trees or k-Nearest Neighbour

(kNN) algorithms in such cases can give misleading

accuracy scores. Lui et al (2003) proposes a Biased

SVM method for learning from positive and

unlabeled examples. This method doesn’t require

any negative examples to be present in the training

set. SVMLight (Joachims, 2008) is another example

which allows for the adjustment of the parameters

associated with the positive and negative examples.

Wang and Japkowicz (2010) used a similar

approach, proposing an algorithm that boosts the

SVM by increasing the misclassification cost

making the parameter associated with the negative

classes a larger negative.

Multiclass Classification

Once the relevance of articles to the overarching

topic has been established, they can now be

categorised according to the sub themes. This is

done through the use of multi-class classifier

techniques. There are generally three types of multi-

class classification methods. These are: extensions

from binary classification methods, the second type

converts multi-classes into sets of binary classes in

order to use binary classification methods, and

finally hierarchical classification (Aly, 2005; Khan

et al, 2010; Mehra and Gupta, 2013).

One of the extensions from binary classification

is the extension of Naive Bayes by using Error-

Correcting Output Codes (Rennie, 2001). Song and

Roth (2014) propose a data less hierarchical data

classification method that requires no supervision or

pre labelling of data. Multiple improvements have

been made to the to the highly accurate SVM

techniques (Ramesh and Sathiaseelan, 2015).

In order to assess document clustering into one

class, a similarity analysis is undertaken. The well-

known methods are Euclidean distance, Manhattan

distance, Canberra distance metric, Cosine

similarity, Bray-Curtis similarity measure, Jaccard

coefficient among others (Lin and Jain, 2008).

Cosine Similarity is one of the most widely used

techniques. Ahlgren and Colliander (2009) give a

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comparison of five document similarity approaches

as well as a comparison of feature selection

techniques. Their experiment was conducted on both

article content as well as citation text. The results for

the content text on its own were better than the

combination of the text and the citation. Tf-idf gave

the best performance for the second order

similarities. Sachdeva and Kastore (2014)

conducted a survey of text similarity measures for

articles in both English and Hindi. They found that

the quality of the clustering depends on the

construction of the tf-idf matrix as well as the

similarity measure used. They also found that the

cosine similarity measure performed fairly well and

it was also better suited for the English language

articles. Cosine similarity was used in the

methodology for this paper based on successes

shown throughout literature for similar types of

cases.

One of the common issues that arise from multi

class classification is class imbalance. Boosting the

data set is another way of addressing the

aforementioned issue. Nonnemaker and Baird

(2009) looked at how safe it is to use synthetic data

to train a classifier. In this experiment, they used

convex interpolation to generate synthetic data for a

character recognition system. Overall, they found

that using interpolated data did not reduce the

accuracy of the classifier and frequently improved it.

Varga and Bunke (2004) conducted a similar

experiment for a handwriting recognition system and

found that synthetic data consistently improved the

accuracy when compared to the accuracy of just

using natural data. Kubat and Matwin (1997)

touches on the commonly used solutions for dealing

with class imbalance in a dataset. They suggest both

synthetically generated data and over-sampling as

ways to balance the classes. Chalwa et al (2002) go

further and suggest over-sampling the minority class

using synthetic data to the training set; they found

that this method worked better than over-sampling

with replacement. Fine et al (1998) gives an in-depth

look at the hidden Markov model (HMM) and its

applications. The model they developed builds a

multi-level structure for English text. Bahl et al

(1983) also gives a summary explanation for how

Markov source models are used to generate probable

texts based on a corpus (Jelinek, 1985).

2.1.4 Visual Analytics of Classified

Geospatio-Temporal News Articles

Geospatial Visual Analytics, a sub field of visual

analytics that focuses specifically with complexities

inherent in geospatial or spatio-temporal data, is

referenced in this paper as it provides background

into how to go about deriving relevant mechanisms

for visualising and interrogating the data in order to

get answers to the subject questions. Visual analytics

is the science of analytical reasoning supported by

interactive visual interfaces (Thomas and Cook

2006). The purpose of visual analytics is to give the

human the opportunity to interrogate the system by

interacting with the data in order to get insights into

the subject matter and enable decision making

(Keim et al, 2008).

An expected outcome of the system is to provide

a meaningful way to visual and synthesis the

resultant classified news articles in order to support

decision making. Several geospatial visual analytics

frame works have been proposed over time. The

choice of framework in this study was informed by:

the need to understand the data that will be

displayed, the need to provide different types of

related visualisations and interactions, and finally

the selection of appropriate visualisation symbology

and styling. News articles are a form of dynamic

data as new articles will be generated from time to

time, a framework that considers dynamic data, its

temporal existence as well as display style and

expected interactivity and analysis is thus considered

for developing visualisations in this study (Sibolla et

al, 2016).

3 SPATIOTEMPORAL CONTEXT

EXTRACTION AND VISUAL

ANALYSIS OF OCEANS AND

COASTS INFORMATION

The approach presented in this paper seeks to

provide a fully automated processing chain that

begins with discovery of news to providing

information insights derived from the news. Our

topic of interest is the South African marine

environment, including the oceans and coasts. The

system discovers relevant news from known local

media sources as well as any available sources

across the World Wide Web. The harvested news

articles are further tested for relevance in terms of

location and topic. The news that pass the relevance

test are further analysed, classified, stored in a

relational database and prepared for visualisation.

The visualisations present insights about predefined

questions relating to the incident occurrence,

location of incident, temporal aspects of the incident

and the cause of the incident.

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3.1 Overall Architecture

Figure 1 below describes the workflow of the

information processing chain.

Figure 1: Process flow.

The following sections will present the method

in more details followed by results achieved. The

paper will end with a discussion of the results and

future work to be done.

3.2 Data Sourcing

The initial sample dataset was a compiled CSV file

that showed all the required information about a

news events in order to address the problem

statement. The data in this file was later used to

derive the training dataset. The table was formatted

as seen in figure 2.

The sourcing of data was done through

information mining using a web scraper. The ideal

set of features of interest in each news articles were:

URL, publication date, harvest time, authors, title,

text, media (Images and videos), news source,

locations, people, and organizations. However, not

all features could be found for all articles. The

article URL, text and harvest time were guaranteed

to be populated for each article. The article URL

was used as an input that gives a reference to the

article being harvested. The text of the news article

was extracted from the body of the HTML page and

the harvest time was taken as a time stamp made

during the scraping process.

Within the scraper, the publish date, title and

authors have no generic way of being parsed,

although rules for parsing each website and its

elements can be created. This is a time consuming

process that also restricts one from dynamically

harvesting different news websites simultaneously.

The documents metadata fields that were extracted

from the HTTP Header, provide a means of

retrieving this “missing” information. Most online

news publishers do include these fields as they can

be fed in to RSS feeds. Extraction of other

supporting media elements related to the article such

as videos or photos was done using the relevant html

tags. These elements were not always found in all

news articles; hence they were treated as supporting,

optional data.

Incoming data was parsed using a Tornado web

server which is sufficient for maintaining multiple

long polling connections. The server was connected

by a WebSocket to the Django Postgres/PostGIS

database server. The server checks for new articles

based on the configured time interval and if new

data was found, the available information was

extracted from the headers. These headers are not in

a standard format, which warranted customised rule

based extraction of desired fields before parsing

them.

3.3 Data Ingestion

This section describes the point when a new article

is detected and how it is prepared for data analysis

based on machine learning. This level of pre-

processing is performed using the Newspapers 3K

python library.

The required input for this step is the URL for

the paper source. The article document is then

downloaded by Newspapers 3K. The purpose is to

deconstruct the article into component elements

namely: Article text, pictures, date of publication

which are all packaged into the processes output

object.

Newspapers3K also allows for configuration of

additional parameters when building a news source

such as the language of the source news documents.

The library saves processing time by caching the

results of the last news document retrieval session,

to avoid processing of duplicate articles.

3.4 Data Analysis

The parsed article text from the data ingestion step,

was categorised as either relevant or irrelevant to the

topic of coastal news. The relevant documents were

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215

Figure 2: Structure of the data set.

then analysed using three concurrent methods in to

extract the information that was used for the visual

analysis. Firstly, the location is extracted using a

Named Entity Recognizer(NER); the coordinates

associated with the location names given by the

NER are found using geocoding, a method of

converting place names and addresses to geographic

coordinates. Secondly, the Natural Language

Processing (NLP) module Newspapers 3k is used to

create a summary of the article as well as provide

keywords. Finally, the text is put into one of the five

existing sub-categories which correspond to coastal

news themes.

3.4.1 Assessing Relevance of News to Topic

Assessing the relevance of the article to coastal news

was done by use of binary classification. The

classification of incoming articles as either coastal

news or not was done using a Support Vector

Machine (SVM) model. An SVM is a discriminant

classifier that separates classes by finding the

hyperplane that maximises the margin between the

different classes. In the case of binary classification,

the SVM would ideally find the hyperplane that

separates the word vectors of the text related to

coastal news from vectors of all other texts. Since

the goal was to separate one class of text from all

others that exist, a one class SVM was found to be a

better fit for this task than a cosine similarity

measure.

In order to potentially save time and computing

power, only the headlines of articles were used to

classify incoming articles as either related to coastal

news or not. The training set used consisted of an

equal number of positive samples about coastal

headline news and negative samples. The text of the

headlines were turned into word tokens using SK-

learn. The punctuation and stop words were

removed using the port stemmer, which also reduced

the derived words to their root. The word tokens that

were generated were then turned into word vectors

using the tf-idf feature selection method. These

word tokens were separated into two classes using

an SVM model from SK-learn trained on the

headlines from the training set. When this model

was tested on the unseen headlines in the testing set,

it accurately predicted the category to which the

headline belonged for 87% of the headlines.

An alternative method using IBM Watson online

resource was employed in order to verify the results

of our chosen method. This methods was used with

the same training and test sets. The training set

consisted of a comma-separated values (CSV) file

with the headline text in the first column and the

category it belonged to in the second column. The

CSV was then uploaded to the IBM Watson web

application where a model was trained on the given

data. To test the model, a second CSV consisting

only of the unlabelled headlines of the training set

was uploaded and the site returned its predictions.

Checking these against the labelled headlines of the

test set gave the much higher accuracy of 97%.

Having filtered out the irrelevant news articles,

the next step was to extract locations and

organisations. The primary entity to be extracted

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was location with organisation being extracted as a

secondary entity. The text from the articles that were

classified as relevant was used as an input to the

Named Entity Extraction (NER) step.

3.4.2 Extraction of Location Context

The Stanford Named Entity Recognizer (NER) was

used in this case for the purpose of extracting

location names. The article’s headline and text were

put through the Stanford parser in order to tag parts

of speech in each sentence. The Stanford NER

Tagger was applied on tokenized words. The tagger

used in this case was a three class model for

“persons”, “locations” and “organisations”. Further

processing was done in order to extract noun chunks,

for example “Cape Town” needs to be recognised as

one entity rather than “Cape” and “Town” which are

two separate entities. This was facilitated by the use

of Beginning inside outside (BIO) tagging followed

by Natural Language (NL) tree generation. The noun

phrases were collected by joining the leaves in the

subtrees of this tree. The context decision tree was

as illustrated in figure 3.

Geocoding assisted in limiting the locations

given by the NER to those within the bounds of

South Africa. In some cases the Stanford NER gave

the prepositions associated with the location,

allowing for a better understanding of the context in

which the location was mentioned. In the cases

where this is possible, the location specifically

related to the event can be predicted more

accurately, otherwise, all the locations mentioned in

the article are taken to be relevant.

3.4.3 Pre Processing of Document Text and

Feature Selection

The Natural Language Processing (NLP) module

Newspapers 3k was used to create a summary of the

article as well as provide keywords. Further

processing of the text was also done using Sci-Kit

Learn.

Using Sci-Kit Learn’s Natural Language Toolkit

(NLTK) the article text was tokenized and the

tokens were normalized by removing all the

punctuation and stop words. According to Python

Newspaper documentation, known stop words

include words such as “the, is, at, which, and on”

(Ou-Yang, 2017). Additionally, the inflected and

derived words were reduced to their roots by suffix

stripping and stemming. This process was

alternatively performed using the Newspapers 3K

python library. The remaining word tokens were

then vectorised according to the tf-idf feature

selection method also using an SK-Learn module.

3.4.4 Allocation of News Articles to

Preassigned Categories

The final classification performed was to assign the

news article into one of the categories corresponding

to coastal news themes. Initially, the collection of

articles was manually divided into several sub-

categories. This was done to allow the user to filter

according to their specific interests, for example, a

user may only be interested in instances of the sea

level rising which could also indicate public

concern. One of the goals of the system is to

automate this sub-categorisation which was done by

using a multi-class classifier. The amount of articles

was not very large, therefore, in order to try and

adjust for the problems that are caused by the small

dataset, the initial 7 subcategories were reduced to 5,

allowing for more articles to be absorbed into the

categories with very few texts. Once the 5 categories

had been finalised, the articles were used to create 5

corpora that could be used to synthetically generate

more articles. The TF-IDF feature selection method

was once again employed before using the cosine

similarity measure to categorise the incoming

articles.

Term frequency (

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times a specific term appears in a document, while

the Inverse Document Frequency is given by:

  

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

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(1)

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3.4.5 Reduction of Classes and Clustering

Initially, the set of articles were classified by hand

into a number of appropriate topics based on the

judgement of the human classifier. These classes

were as listed in table 1.

The small size of the data set along with the

discrepancy in the number of articles across the

classes was a cause for concern when it came to

training the model. These problems were further

exacerbated by the fact that the text of 5 of the

original articles could no longer be accessed from

the article URL. In order to correct for the uneven

distribution of articles across the classes, articles

with overlapping topics were clustered together and

the number of classes reduced to five. The clustering

was performed with the help of a Latent Dirichlet

Allocation analysis and visualization.

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217

Figure 3: Decision tree for the context of extracted locations.

Table 1: Original Set of Categories.

Gensim was used to generate a corpus from the

text of the articles, along with a dictionary and

similarity index. pyLDAvis was then used to

visualise the topics. Based on the visible overlap

amongst topics and also using some human

supervision, the remaining five classes were as listed

in table 2.

Table 2: Revised Set of Categories.

3.4.6 Data Augmentation

Ideally the data set would be large enough to allow

for a subset of training set to be used for a more

thorough initial test of the classifier, this is what is

known as the the cross-validation set. In this case the

articles were only divided into a training set and a

test set as there were not enough articles to allow for

a cross-validation set, so that testing was only done

on the unseen data in the test set. The training set

consisted of thirty five of the original articles. The

reasoning behind this was that the category with the

smallest number of articles (waste) only had eleven

articles, so in order to make sure that there were a

few waste articles in the test set while keeping the

number of articles from each category in the training

Class

Number of articles

Illegal Fishing

Development

Red Tide

Waste

Natural Event

Sea Life

Public Concern

Class

Number of Articles

Illegal Fishing

Public Concern

Sea Life

Red Tide

Waste

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set equal, seven original articles from each category

were added. For each category, twenty synthetic

articles were created using Markovify with a two

word seed. Each synthetic article had between ten

and fifteen sentences with a maximum sentence

overlap of seventy percent (70%). These synthetic

articles were added to the training set.

The articles chosen to form part of the training

set had their article text written to one of five “.txt”

files, each bearing the name of the article’s

classification. This means that the training set

consisted of five “.txt” files containing all the text

from the articles that were initially tagged.

The cosine similarity technique was used to

cluster and classify the documents into the five

classes.

3.4.7 Cosine Similarity

The same feature selection method was used for

each article in the test set. This set of word vectors

was then compared to the sets of word vectors in the

training set by measuring the scalar product of the

vectors between them. Dividing the result by the

product of the magnitudes of the vectors gave the

cosine of the angle between them and, by extension,

the cosine similarity. Mathematically, this is:

 





   

  



















(2)

Where: a = article being evaluated and b = the

whole article corpus. The test articles were then

tagged with the name of the file that they had the

highest similarity to, which in this case, is also their

most probable category.

3.5 Storage of Relevant Data Records

Creating a database enables the data to be stored in

an organised way which then enables easy access by

the methods downstream from data ingestion. The

data stored in the database was a result of the

information derived from the scraper, augmented

with information that was extracted from the news

header file. Once an article has been accepted by the

binary classifier, the relevant information was stored

in a Postgres database, along with information

extracted from the news header file.

4 RESULTS

This section discusses results of the machine

learning based data classification process and the

visual analytics results as the major findings.

The visualisation of the five classes which arose

as a result of the reclassification of the articles from

the original seven classes was done using

pyLDAvis. The classes are distinct when modelled

according to their topics. This confirms that no

further clustering is required and these are the final

classes.

Due to the fact that the system had not

accumulated a lot of data, classification became a

challenge. In order to improve the accuracy of the

classifier, data amplification by adding synthetic

data was done. This is an acceptable method as

noted in the literature review. In order to ensure that

adding the synthetic data improved the accuracy of

the classifier a few tests were run with an increasing

number of synthetic articles added to the training set.

The table below shows how the accuracy increased

as a result, using cosine similarity.

Table 3: Effect of Generated Articles on Accuracy Score.

Added synthetic

articles

Accuracy Score

(%)

73.91

91.30

93.48

95.65

97.83

5 VISUALISATION OF

RESULTING DATA

The visualisation of data was done using three key

types of displays; the map is used to emphasise

spatial relationships, the graphs are meant to

highlight the temporal aspect of the data and show

possible trends. A bubble chart assigns distinct

colours to each news provider in order to

differentiate amongst the sources of news; this helps

the user identify the largest media contributor to

each topic. Colour and size, were used across all

display types in order to enhance the perception of

the categories and data variables. According to

Bertin’s retinal properties, the map graph primitives

individually did not provide an intuitive perception

of the data, therefore the map and graph views were

An Automated Approach to Mining and Visual Analytics of Spatiotemporal Context from Online Media Articles

219

Figure 4: Visual Analytics presentation of the classified documents.

linked to enable focused analysis by the user such as

selection and filtering of data.

Every article that has an associated geolocation

is displayed as a point on an OpenLayers 3 map,

which forms the centrepiece of the application.

When one of these points is selected, additional

information is displayed on a panel next to the map

including the entities mentioned, keywords, an

article summary and a link to the full article. These

points are colour-coded by category and filters for

these categories are placed on the left navigation bar.

A time-series chart based on publication date

allows the user to get a sense of monthly frequency

of events and a bubble chart of news sources shows

us which outlets contributed the most to our corpus.

Both of these charts can be used as filters, and all

three filters in the application are linked to the same

state, a linked view effect is achieved between them.

Using the “red tide” category filter, we were able to

notice a seasonal trend in the reporting of the

phenomenon.

6 DISCUSSIONS

The purpose of this study was to provide an

automated approach to mining and intuitive

visualisation of spatiotemporal context from web

based text documents. The necessary steps towards

achieving this have been illustrated and discussed.

Procedure involves acquisition of media files, pre-

processing to the stem and removing suffixes,

extraction of named entities and classification. The

intuitive visualisation approach applied follows

methodologies for visual analytics of dynamic

(streaming) geospatial data.

Based on the method described in this paper,

automation of the process of deriving spatiotemporal

context from news media and developing intuitive

visualizations that support visual analytics was a

success. Along the South African coast a correlation

was found between a harmful algal bloom (red tide)

event detection which was performed using satellite

imagery, and news on rock lobster walkout in the

Western Cape seas that was reported in local

newspapers captured in the system. This was an

accepted as the news were validated by residents of

the area and the harmful algal bloom detection was

also validated through satellite imagery.

7 FUTURE WORK

The validation event has prompted a need to extend

the methodology for analysis of temporal and spatial

correlation of events reported in the news, as well as

correlation and fusion of news events with other

coinciding spatiotemporal detected events.

Apart from correlation analysis it is also

important to assess where the news that have been

GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management

220

reported portray a positive picture or whether they

are negative. As a result sentiment analysis will be

performed. Sentiment analysis also known as

opinion mining involves the process of extracting

emotion or attitude of the writer, in order to

determine if the written piece is positive, negative or

even neutral.

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