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Keyword Detection Techniques: A Comprehensive Study

February 2018
Engineering, Technology and Applied Science Research 8(1):2590-2594

Authors:

Institute of Business Administration Karachi

Automatic identification of influential segments from a large amount of data is an important part of topic detection and tracking (TDT). This can be done using keyword identification via collocation techniques, word co-occurrence networks, topic modeling and other machine learning techniques. This paper reviews existing traditional keyword extraction techniques and analyzes them to make useful insights and to give future directions for better automatic, unsupervised and language independent research. The paper reviews extant literature on existing traditional TDT approaches for automatic identification of influential segments from a large amount of data in keyword detection task. The current keyword detection techniques used by researchers have been discussed. Inferences have been drawn from current keyword detection techniques used by researchers, their advantages and disadvantages over the previous studies and the analysis results have been provided in tabular form. Although keyword detection has been widely explored, there is still a large scope and need for identifying topics from the uncertain user-generated data.

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Engineering, Technology & Applied Science Research Vol. 8, No. 1, 2018, 2590-2594 2590

www.etasr.com Shaikh : Keyword Detection Techniques: A Comprehensive Study

Keyword Detection Techniques

A Comprehensive Study

Zaffar Ahmed Shaikh

Faculty of Computer Science & Information Technology

Benazir Bhutto Shaheed University

Lyari, Karachi, Pakistan

zashaikh@bbsul.edu.pk

Abstract—Automatic identification of influential segments from a

large amount of data is an important part of topic detection and

tracking (TDT). This can be done using keyword identification

via collocation techniques, word co-occurrence networks, topic

modeling and other machine learning techniques. This paper

reviews existing traditional keyword extraction techniques and

analyzes them to make useful insights and to give future

directions for better automatic, unsupervised and language

independent research. The paper reviews extant literature on

existing traditional TDT approaches for automatic identification

of influential segments from a large amount of data in keyword

detection task. The current keyword detection techniques used by

researchers have been discussed. Inferences have been drawn

from current keyword detection techniques used by researchers,

their advantages and disadvantages over the previous studies and

the analysis results have been provided in tabular form. Although

keyword detection has been widely explored, there is still a large

scope and need for identifying topics from the uncertain user-

generated data.

Keywords-keyword detection; information retrieval; topic

detection; machine learning; comprehensive study

I. INTRODUCTION

Keyword extraction using manual methods is slow,

expensive and bristling with mistakes [1]. In recent years,

many automatic bursty keyword extraction techniques have

been proposed to extract keywords from large amounts of data.

These keywords are helpful in identifying themes and

influential segments and framing semantic web and other

applications of natural language processing [2, 3]. Automatic

keyword detection research area is related to topic detection

and tracking (TDT) domain which was proposed in [4].

Various applications use keyword extraction techniques for

web search, report generation and cataloguing [5]. This area is

intended to identify the most useful terms which include many

sub-processes. Documents are introduced in MS Word, html, or

pdf formats. Initially, the documents are pre-processed to

remove redundant and unimportant information [6, 7]. The data

is then processed through different keyword extraction

approaches including statistical approach, linguistic approach,

machine learning approach, network based approach and topic

modelling approach [8, 9].

In statistical approach, term frequency–inverse document

frequency (Tf-Idf) is the most widely used technique for

keyword extraction. The researchers use Tf-Idf to give a

document a score based upon some query. A change in score

occurs when a query is changed or updated. Without a query,

there is no score [10]. Recently many new techniques have

been developed for statistical keyword extraction [11]. These

include PageRank, LexRank, etc. In PageRank, the researchers

assign a score to a document based upon the documents it links

to, and the documents which link to it. It is a global ranking

scheme [10]. Therefore, in PageRank, the score does not

change (like in Tf-Idf) depending on the query used. As

observed, PageRank and LexRank algorithms perform better

than Tf-Idf. In linguistic approach, automatically identifying

keywords is similar to semantic resemblance [12]. In machine

learning approach, the keyword extraction technique is

considered as classification technique [13].

Different dictionaries including WordNet, SentiNet and

ConceptNet are used for keyword extraction techniques. In

network based algorithms, the nature and semantics of word

co-occurrence networks is studied to identify important terms.

In this, nodes are considered as words and edges are considered

as co-occurrence frequency [14]. Many useful insights have

been obtained from these algorithms for identifying influential

segments and keywords. Topic modelling techniques have been

popularized in [15]. Authors introduced Latent Dirichlet

Allocation technique which is used to identify which document

is related to which topic and to what extent [16]. This has been

further improved by Hierarchical Dirichlet Process, Pachinko

Allocation Model, Relational Topic Modeling, Conditional

Topic Random Fields and recently by Hierarchical Pitman–

Yor–Dirichlet Language Model and Graph Topic Model [17].

Although keyword extraction is an important area of research

and many researchers and practitioners gave a lot of attention

to it, state of the art keyword extraction method is still not

observed as compared to many other core natural language

processing tasks [18]. This paper reviews existing traditional

keyword extraction techniques and analyzes them to make

useful insights to give future directions for better automatic,

unsupervised and language independent research.

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II. RELATED WORK

Authors in [19] developed a so-called tool ‘Keyword

Extractor’ for automatic extraction of most likely terms that

closely match experts’ preferences. Their study was related to

brain research which involved worldwide collaborations and

exchange of information among neuroinformatics centers and

portal sites. The main objective of their study was the efficient

use of resources and the improvement in the quality of brain

research. Each center and site developed their own set of

keywords for classification of the main text and the resources.

The researchers tested their tool over the abstract database of

two science journals. Authors in [20] extracted keywords from

a Chinese microblog. To extract keywords, they performed five

steps and used three features (i.e., graph model, semantic space,

and location of words). In the first step, researchers

downloaded microblog API of a user. Secondly, they

preprocessed the data by applying data cleaning, word

segment, POS tagging, and stop word removal techniques. To

extract keywords, researchers in the third step created a graph

model that was based on the co-occurrence between words.

They assigned sequence numbers to the words according to

their location and developed weight of the words by using the

score formula. In the fourth step, researchers first created a

semantic space that was based on topic extraction and then

computed statistical weight of the words by using Tf-Idf. In the

fifth and last step, researchers first identified location of words,

and then, based on the location of those words, computed the

rank value of each word. Authors in [21] focused on the

structure approach and graph generation. The approach used in

this paper is structure based in which researchers created graph

model and identified bursty topics and events. In topic

clustering, twitter tweets were separated to produce

homogeneous graphs and heterogeneous graphs. For

homogeneous graphs, researchers used OSLOM algorithm to

find interaction among users. For heterogeneous graphs,

rankclus algorithm was used to construct a set of tweets ranked

with number. Finally, from both graph results, the concept,

theme or event of a tweet was measured by joining tweets with

the same name. Researchers planned ahead to develop graph

models to be used for different types of events and to construct

a method that can define events.

Authors in [22] developed a keyword extraction technique

for tweets with high variance and lexical variant problems.

Lexical variants are examples of free variation in language.

They are characterized by similarity in phonetical or spelling

form and identity of both meaning and distribution. The

authors used brown clustering and continuous word vector

methods. In brown clustering method, they clustered words

having same meaning (such as no, noo, etc.) and then found out

the features for the individual cluster. In continuous word

vector method, the authors defined a layer by finding its

probability and then the word is changed into continuous word

vector. Next, they predicted the length of the keyword by

calculating the ratio between the number of keywords and the

total number of words in the tweets. In the end, linear

regression method was used to predict the number of

keywords. Authors in [23] developed a system to detect

popular keyword trend and bursty keywords. Their system

detects keyword abbreviations and any typing and spacing

errors. The first step they took is to collect the candidate

keywords (i.e., the first word starting with the capital letter or

the word enclosed in quotation mark is considered as candidate

keyword). The second step was to merge keywords. To do so,

they considered acronyms and typo and spacing errors, and

then, found out the Tf accordingly. Finally, they detected

popular keywords from the candidate keywords which were

merged, and then, selected bursty keywords using the burst

ratio technique. Authors in [24] gave the idea of TOPOL (a

topic detection method based on topology data analysis) which

identifies the irrelevant noisy data from the useful data. The

first step of the authors was the preprocessing step in which the

elimination of the hashtags, the URLs, and the non-textual

symbols from a tweet was done. Their second step was

mapping, in which a matrix was generated by applying the

SVD technique. In the third step, which the authors called the

topic extraction step, the topics were selected based on the

interest. Finally, the results were computed based on topic

recall, keyword precision, and keyword recall parameters.

Authors in [25] presented and discussed different methods and

approaches used in the keyword extraction task. They also

proposed a graph based keyword extraction method for the

Croatian language which is based on extraction of nodes. The

authors used selectivity-based keyword extraction method in

which text is represented in the form of vertex and edges. The

result is computed on the in-degree, out-degree, closeness and

selectivity. Authors in [26] developed a keyword extraction

method that represents text with a graph, applies the centrality

measure and finds the relevant vertices. Authors proposed a

three-step based technique called TKG (Twitter Keyword

Graph). The first step was the pre-processing in which stop

words were removed. In the second step, a graph was

developed in which nearest neighbor and all neighbors were

considered. Finally, the results were computed based on the

precision, recall, F-measure test scores and graph scalability.

Authors in [27] proposed an information summarization

method for the large quantum of information which is

disseminated everyday through tweets. Their method collects

tweets using a specific keyword and then, summarizes them to

find out the topics. The authors provide two algorithms: Topic

extraction using AGF (TDA) and topic clustering and tweet

retrieval (TCTR). The methodology first extracts tweets from

twitter and then applies the Tf-Idf technique to find out weights

and word frequency. The AGF is evaluated using keyword

rating. Finally, the results are calculated based on the class

entropy, purity, and cluster entropy. Authors in [28] proposed a

technique in which a user can search using a search engine but

without entering any keywords. The google similarity distance

technique is used to find the keywords. A log is maintained in

which user behavior and repository is saved. So, the need for

the repository is abolished and everything is done online and in

real time. Keyword expansion and extraction methods are used

to extract relevant and accurate information. In keyword

expansion, help is provided to user to enter the exact keyword

and to get the exact information. In keyword extraction, the

word is analyzed based on the occurrence on the length and

frequency. Keyword extraction method relies on statistical

approaches and machine learning approaches. The proposed

methodology of the authors is composed of three parts: 1-g

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filtering, google similarity distance calculation, and search

results filtering. Finally, the results are calculated based on the

parameters of precision and recall. The relationship between

top k results is evaluated. Thus, the authors proposed a system

in which user just needs to browse the web page and the

relevant keywords are generated. The system suits well for the

science stream as the words are clear but may not be accurate

for the social science.

Authors in [29] produced a facility based on Bayesian text

classification approach called high relevance keyword

extraction (HRKE) to extract the keywords at the stage of

classification without the use of pre-classification process. The

facility uses a posterior probability value to extract keywords.

The HRKE first extracts the words from the text. Next, the

posterior probability is calculated. Finally, the Tf-Idf method is

used to assign weights to words. Authors claim that the HRKE

facility improves the performance and accuracy of the Bayesian

classifier and reduces time consumption. The experiment was

conducted on three dataset-featured article datasets. In the end,

the corresponding threshold and accuracy graph is plotted.

Authors in [30] address the problem of part-of-speech (POS)

tagging from the richer text of twitter. Authors developed a

POS tagset first. Secondly, they performed manual tagging on

the dataset. Afterwards, the features for the POS tagger were

developed. Finally, the experiments were conducted to develop

the annotated dataset for the research community. The

hashtags, URLs, and emotions were considered. The results

were obtained with 90 percent accuracy. Authors concluded

claiming that the approach can be applied to linguistic analysis

of social media, and the annotated data can be used in semi

supervised learning. Authors in [31] gave a solution to the

problem of statistical keyword extraction from the text by

adapting entropic and clustering approaches. Authors made

changes in these approaches and proposed a new technique

which detects keywords as per user’s needs. The main

objective of the authors was to find and rank important words

in the text. The two approaches were applied on short texts

(such as web pages, articles, glossary terms, generic short text

etc.) and long texts (such as books, periodicals etc.). Results

were evaluated and the clustering approach proved to be better

for both cases, while the entropic approach suited well for the

long text and did not perform well for the partitioned text.

Authors in [32] proposed a metric called entropy difference

(ED) for the ranking of the words on a Chinese dataset.

Authors used Shannon’s entropy method which is the

difference between intrinsic and extrinsic modes. The idea of

intrinsic and extrinsic modes is that meaningful words are

grouped together. Therefore, the words are extracted and

ranked according to the entropy difference. Authors calculated

mean, mode and median on entropy differences. Their ED

metric proved to be a good choice in word ranking. The

method differentiates between the words that define authors’

purpose and the irrelevant words which are present randomly in

the text. This method is well suited for single document of

which no information is known in advance. Authors in [33]

provided a solution to the inherent noisy and short nature tweet

problem of Twitter streams called HybridSeg. Authors

incorporated local context knowledge of the tweets with global

knowledge bases for better tweet segmentation. The tweet

segmentation process was performed on two tweet datasets.

The tweets were split into segments to extract meaning of the

information conveyed through the tweet. Results show that

HybridSeg significantly improved tweet segmentation quality

compared with other traditional approaches. Authors claim that

the segment based entity is better than word based entity.

Author in [34] provided a unique solution to the keyword

extraction problem called ConceptExtractor. The

ConceptExtractor do not decide on the relevance of a term

during the extraction phase, instead, it only extracts generic

concepts from texts and postpones the decision about relevant

terms based on the needs of the downstream applications.

Authors claim that unlike other statistical extractors,

ConceptExtractor can identify single-word and multi-word

expressions using the same methodology. Results were

evaluated based on three languages. Precision and recall were

used for the result evaluation. Authors also defined a metric to

specificity both single and multi-word expressions usable in

other languages. Authors in [35] considered various Chinese

keyword extraction methods. In this paper, extended Tf

approach has been defined which considers Chinese

characteristics with Tf method. Authors also developed a

classification model based on support vector machine (SVM)

algorithm. Many improvement strategies were defined and four

experiments were performed to evaluate the results. Results

showed that SVM optimized the keywords. Precision and recall

rate improved much better. Authors concluded that the

improved Tf method is much better than the traditional Tf

method in terms of accuracy and precision. Authors in [36]

discovered and classified terms that are either document title or

‘title-like’. Their idea was that the terms that are title or title

like should behave in the same way in a document. The

classifier was trained using distributional and linguistic features

to find the behavior of the terms. Different features were

considered such as location, frequency, document size etc. The

rating was calculated on the basis of topical, thematic and title

terms. After this the evaluation was performed based on recall

and precision. The recall rate of finding the title terms was high

but the precision rate was low because some of the words

which were not titles were also identified in title terms. Authors

in [37] developed a sensitive text analysis for extracting task-

oriented information from unstructured biological text sources

using a combination of natural language, dynamic

programming techniques and text classification methods. Using

computable functions, the model finds out matching sequences,

identifies effects of various factors and handles complex

information sequences. Authors pre-processed the text contents

and applied them with entity tagging component to find out the

causes of diseases related to low-quality food. Results show

that the bottom-up scanning of key-value pairs improves

content finding which can be used to generate relevant

sequences to the testing task. The method improves

information retrieval accuracy in biological text analysis and

reporting applications.

III. ANALYSIS OF KEYWORD EXTRACTION APPROACHES

Table I provides inferences drawn from modern keyword

detection techniques, their advantages and disadvantages over

previous studies, and result analysis.

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TABLE I. ANALYSIS OF EXISTING KEYWORD DETECTION TECHNIQUES

Paper Techniques used Advantages Disadvantages Results / Analysis

[20] a) Graph model

b) Semantic space

a) Can detect the words which are

wrongly segmented.

b) Extracts keywords from a micro

blog.

a) Not suitable for large texts.

b) Some terms will not be

distinguished.

Best performance obtained is

0.6972

[21] a) OSLOM algorithm

b) Page rank algorithm

a) Able to identify the topics of twitter

event.

b) Less expensive.

Not able to identify the events

based on graph clusters. Best result obtained from

structured based approach.

[22] a) Brown clustering

b) Continuous word

vector

a) Improved state of the art for keyword

extraction.

b) Automatically keyword extraction.

Not suitable for Facebook text

keyword extraction. Accuracy for precision

obtained is 72.05, recall 75.16.

[24] TOPOL a) Suitable for noisy data.

b) Reduces computation time and

improves topic extraction result.

Suffers from data

fragmentation. The result obtained is 0.5380

for recall,0.7500 for precision.

[26] a) Tf-Idf

b) KEA

c) Proposed TKG

a) TKG proved to be robust and

superior compared to other approaches

b) TKG is simpler to use than KEA

The best configuration of TKG

was not found TKG results better compared to

KEA and Tf-Idf.

[28] a) Statistics approach

b) Machine learning

approach

a) Search engine which can

automatically extract important

keywords

b) System works well

Not suitable for business

management domain High recall rate

[29] Bayesian approach

a) Low cost, simple and efficient

method.

b) Handles raw data without text

preprocessing.

a) Presence of noisy data may

degrade the performance.

b) Feature selection method

degrades the efficiency of

classification task.

Improved accuracy

[31] a) Entropic

b) Clustering approach

a) Suitable for both long and short

texts.

b) Reliable obtained results.

Median and mode did not give

the correct result. Good clustering results for

both short and long texts.

[32] Shannon entropy

a) Suitable for text with no information

known in advance.

b) Easy to numerically implement.

Median and mode did not give

the correct result. Better results for single

document.

[33] Hybrid segmentation High quality tweet segmentation. Manual segmentation is

expensive. Improved precision.

[34] Statistical language

independent Good for extracting single and multi-

word expressions. Not suitable for long text Improved precision and recall.

[36] a) Decision tree

classifier

b) Pattern recognition Easy title determination

a) Not easy to determine the

best document size.

b) Precision was not significant

than recall.

Recall 85% was achieved for

title like terms.

[37] a) Sensitive text analysis

b) Context-based

extraction method

a) Category-oriented approach for

extraction of task-specific information

b) Investigations into recall and

precision were carried out.

Not tested on generic data.

a) Food safety is analyzed to

prevent future consequences.

b) Improved classification

accuracy by utilizing

optimization constraints.

c) Causes of diseases related to

low-quality food were

identified.

IV. CONCLUSIONS

This paper extends understanding of widely used `existing

approaches to keyword detection in the identification of

influential segments from a large amount of textual data or

documents. Therefore, extant literature on existing traditional

TDT approaches to automatic identification of important words

was reviewed and discussed. Techniques reviewed include

collocation, word co-occurrence networks, topic modelling and

other machine learning approaches. Results show that the

majority of these techniques is domain dependent and language

dependent. It was observed that although traditional keyword

extraction techniques have been performing satisfactorily, a

need exists to propose unsupervised, domain independent and

language independent techniques which use statistically

computational methods. Keyword extraction task has been

widely explored, but there is still a large scope and gap for

identifying topics from the uncertain user-generated data.

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AUTHOR PROFILE

Dr. Zaffar Ahmed Shaikh received his PhD in Computer Science from the

Institute of Business Administration, Karachi (IBA-Karachi) in 2017. He is

currently working as an Assistant Professor at Benazir Bhutto Shaheed

University, Lyari, Karachi, Pakistan. He has twenty-three research

publications to his credit and has received several research grants from EPFL

(Switzerland), Higher Education Commission (Pakistan), Ministry of Higher

Education (KSA) and IBA-Karachi. His research interests include Data

Sciences, Knowledge Management, Language & Technology, Learning

Environments, MOOCs, Social Software, Technology Enhanced Learning etc.

Dr. Shaikh is a professional member of ACM and IEEE.

Open source intelligence extraction for terrorism‐related information: A review

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Full-text available

Jul 2022

In this contemporary era, where a large part of the world population is deluged by extensive use of the internet and social media, terrorists have found it a potential opportunity to execute their vicious plans. They have got a befitting medium to reach out to their targets to spread propaganda, disseminate training content, operate virtually, and further their goals. To restrain such activities, information over the internet in context of terrorism needs to be analyzed to channel it to appropriate measures in combating terrorism. Open Source Intelligence (OSINT) accounts for a felicitous solution to this problem, which is an emerging discipline of leveraging publicly accessible sources of information over the internet by effectively utilizing it to extract intelligence. The process of OSINT extraction is broadly observed to be in three phases (i) Data Acquisition, (ii) Data Enrichment, and (iii) Knowledge Inference. In the context of terrorism, researchers have given noticeable contributions in compliance with these three phases. However, a comprehensive review that delineates these research contributions into an integrated workflow of intelligence extraction has not been found. The paper presents the most current review in OSINT, reflecting how the various state‐of‐the‐art tools and techniques can be applied in extracting terrorism‐related textual information from publicly accessible sources. Various data mining and text analysis‐based techniques, that is, natural language processing, machine learning, and deep learning have been reviewed to extract and evaluate textual data. Additionally, towards the end of the paper, we discuss challenges and gaps observed in different phases of OSINT extraction. This article is categorized under: Application Areas > Government and Public Sector Commercial, Legal, and Ethical Issues > Social Considerations Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

Optimization of scientific publications clustering with ensemble approach for topic extraction

Article

Mar 2023

The continually developing Internet generates a considerable amount of text data. When attempting to extract general topics or themes from a massive corpus of documents, dealing with such a large volume of text data in an unstructured format is a big problem. Text document clustering (TDC) is a technique for grouping texts based on their content similarity. Partitioning text collection based on the documents’ content significance is one of the most challenging tasks at TDC. This study proposes the Bare-Bones Based Salp Swarm Algorithm (BBSSA) to solve the problem of TDC. In addition, to extract the topics from the clusters, an ensemble approach for automatic topic extraction (TE) is proposed. The proposed BBSSA and the ensemble TE approach are tested using six standard benchmarks and six scientific publishing datasets from top QS ranking UAE universities. BBSSA’s findings are compared with sixteen well-known techniques, including eleven metaheuristic algorithms, such as the Whale Optimization Algorithm (WOA), Firefly Algorithm (FFA), Bat Algorithm (BAT), Harmony Search (HS), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Multi-Verse Optimizer (MVO), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO), Krill Herd Algorithm (KHA), SSA, and five clustering methods, such as K-means++, K-means, Density-based Spatial Clustering of Applications with Noise (DBSCAN), Spectral, and Agglomerative. The results of the ensemble TE approach are compared with those of seven well-known statistical methods, including Mutual Information (MI), TextRank (TR), Co-Occurrence Statistical Information-based Keyword Extraction (CSI), Term Frequency-Inverse Document Frequency (TF-IDF), most frequent based keyword extraction (TF), YAKE!, and RAKE. According to the experiments, the BBSSA outperforms all other approaches and is exceedingly competitive. The results also reveal that for most datasets, the proposed ensemble TE strategy outperforms all existing TE methods based on external metrics. Thus, the ensemble TE approach can be seen as a supplement to the other methods.

FACTORS OF DEMAND FOR CENTRAL BANK DIGITAL CURRENCY: EVIDENCE FROM SWEDEN AND SWITZERLAND

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Full-text available

Apr 2022

The article determines factors affecting the demand for Central Bank Digital Currency (CBDC) introduction. The research paper identifies these key parameters to be similar to those contributing to cash demand. Moreover, a comparative analysis between Sweden and Switzerland is presented based on these factors to highlight differences in national economies, which play a significant role in the subsequent demand for CBDC. The timeframe of this analysis is mainly based on the period of the recent COVID-19 pandemic, which brought with itself a global economic crisis. These difficult macroeconomic conditions emphasize the characteristics of national economies that may be evidence of the need of CBDC issuance.

COMPARATIVE CASE STUDY: AN EVALUATION OF PERFORMANCE COMPUTATION BETWEEN SUPPORT VECTOR MACHINE, K-NEAREST NEIGHBORS, K-MEAN, AND PRINCIPAL COMPONENT ANALYSIS

Article

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Apr 2022

The rapid advancement in Information Technology (IT) and the development of Artificial Intelligence (AI) makes systems more efficient and effective in performing day-today tasks, such as identification, extraction, detection, and recognition-related problems. These pose a serious indication towards the concept of Machine learning (ML) and the proposed efficient techniques for distinct purposes, which are either performed artificially supervised, semi-supervised or unsupervised ML. However, the ML systems have the potential to self-learn and adapt themselves by explicitly programmed existence based on earlier experience. ML and data mining approaches are justified in this research. It is featured in the Principal Component Analysis (PCA) method of supervised and unsupervised learning. Both machine learning algorithm approaches use a variety of methodologies. For this purpose, we use the same datasets for classification, regression, and clustering procedures, as well as the PCA data mining technique, to evaluate and perform on the K-Means, K-nearest neighbor (KNN), and support vector machine (SVM) algorithms. Finally, we present simulations that show the estimated criteria, parameters, and efficiency with effective algorithms' performance, and state-of-the-art results.

MTVIS: A FRAMEWORK FOR VISUAL ANALYSIS AND EXPLORATION OF MOBILE MONEY TRANSACTIONS PETER V. ALEKSEEV

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Apr 2022

Mobile money transfer systems (MMTS) in countries with limited banking are increasingly becoming the mainstream banking system. The analysis of transactions performed on these systems helps to detect fraudulent and criminal activities. This paper introduces a novel visual analytics framework for visual analysis and exploration of mobile money transactions. Our system enables the empirical analysis of mobile money transactions data using multiple views to reveal the temporal, geospatial, and categorical aspects of the transactions. Several challenges were identified related to the given MMT datasets through the process of implementing of fraud detection framework. In addition, as a step towards recognizing the difficult task of developing versatile and flexible fraud detection models, this work proposes concepts to address the identified challenges.

Multiclass Skin Cancer Classification using EfficientNets -A First Step towards Preventing Skin Cancer

Article

Full-text available

Dec 2021

Skin cancer is one of the most prevalent and deadly types of cancer. Dermatologists diagnose this disease primarily visually. Multiclass skin cancer classification is challenging due to the fine-grained variability in the appearance of its various diagnostic categories. On the other hand, recent studies have demonstrated that convolutional neural networks outperform dermatologists in multiclass skin cancer classification. We developed a preprocessing image pipeline for this work. We removed hairs from the images, augmented the dataset, and resized the imageries to meet the requirements of each model. By performing transfer learning on pre-trained ImageNet weights and fine-tuning the Convolutional Neural Networks, we trained the EfficientNets B0-B7 on the HAM10000 dataset. We evaluated the performance of all EfficientNet variants on this imbalanced multiclass classification task using metrics such as Precision, Recall, Accuracy, F1 Score, and Confusion Matrices to determine the effect of transfer learning with fine-tuning. This article presents the classification scores for each class as Confusion Matrices for all eight models. Our best model, the EfficientNet B4, achieved an F1 Score of 87 percent and a Top-1 Accuracy of 87.91 percent. We evaluated EfficientNet classifiers using metrics that take the high-class imbalance into account. Our findings indicate that increased model complexity does not always imply improved classification performance. The best performance arose with intermediate complexity models, such as EfficientNet B4 and B5. The high classification scores resulted from many factors such as resolution scaling, data enhancement, noise removal, successful transfer learning of ImageNet weights, and fine-tuning [71, 72, 73]. Another discovery was that certain classes of skin cancer worked better at generalization than others using Confusion Matrices.

A novel ensemble statistical topic extraction method for scientific publications based on optimization clustering

Article

Full-text available

Jan 2021
MULTIMED TOOLS APPL

The automatic topic extraction (TE) from scientific publications provides a very compact summary of the clusters’ contents. This often helps in locating information easily. TE enables us to define the boundaries of the scientific fields. Text Document Clustering (TDC) represents, in general, the first step of topic identification to identify the documents, which address a related subject matter. Metaheuristics are typically used as efficient approaches for TDC. The multi-verse optimizer algorithm (MVO) involves a stochastic population-based algorithm. It has been recently proposed and successfully utilized to tackle many hard optimization problems. In the TE process, the focus of each statistical TE method is placed on various language feature space aspects. The aim of this paper is to design a novel ensemble method for an automatic TE from a collection of scientific publications based on MVO as the clustering algorithm. The automatic TE, which is used in our approach, is term frequency-inverse document frequency (TF-IDF), most frequent based keyword extraction (TF), co-occurrence statistical information-based keyword extraction (CSI), TextRank (TR), and mutual information (MI). A group of candidate topics can be provided by each automatic TE method for the proposed ensemble method. Next, the ensemble approach prunes the candidate topics’ set via the application of a specific filtering heuristic. Then, their scores are recalculated based on the prescribed metrics. After that, for selecting a set of topics for certain scientific publications, dynamic threshold functions are applied. The findings emphasized the refined candidate set’s efficiency, as well as effectiveness. The results also showed that the system’s quality has been improved by new topics. The proposed method achieved better precision, as well as recall on a similar dataset compared to the state-of-the-art TE methods.

Supporting Scholarly Search by Query Expansion and Citation Analysis

Article

Full-text available

Aug 2020

Published scholarly articles have increased exponentially in recent years. This growth has brought challenges for academic researchers in locating the most relevant papers in their fields of interest. The reasons for this vary. There is the fundamental problem of synonymy and polysemy, the query terms might be too short, thus making it difficult to distinguish between papers. Also, a new researcher has limited knowledge and often is not sure about what she is looking for until the results are displayed. These issues obstruct scholarly retrieval systems in locating highly relevant publications for a given search query. Researchers seek to tackle these issues. However, the user's intent cannot be addressed entirely by introducing a direct information retrieval technique. In this paper, a novel approach is proposed, which combines query expansion and citation analysis for supporting the scholarly search. It is a two-stage academic search process. Upon receiving the initial search query, in the first stage, the retrieval system provides a ranked list of results. In the second stage, the highest-scoring Term Frequency-Inverse Document Frequency (TF-IDF) terms are obtained from a few top-ranked papers for query expansion behind the scene. In both stages, citation analysis is used in further refining the quality of the academic search. The originality of the approach lies in the combined exploitation of both query expansion by pseudo relevance feedback and citation networks analysis that may bring the most relevant papers to the top of the search results list. The approach is evaluated on the ACL dataset. The experimental results reveal that the technique is effective and robust for locating relevant papers regarding normalized Discounted Cumulative Gain (nDCG), precision, and recall.

An ensemble topic extraction approach based on optimization clusters using hybrid multi-verse optimizer for scientific publications

Article

Full-text available

Feb 2021

For text document clustering (TDC), a novel hybrid of the multi-verse optimizer (MVO) algorithm and k-means (also called H-MVO) are proposed in this work. Moreover, a new ensemble method for an automatic topic extraction (TE) has been proposed in this paper, from a set of scientific publications in the form of text documents with the purpose of extracting topics from clustered documents. Often, the existing TE methods draw upon the statistical theory. However, the results might be different when the same clustered document is utilized. Consequently, there can be imprecise results, which are related to the extracted topics from the clustered documents owing to the behavior of the TE methods. As a result, the vigorous characteristics of the TE methods are ensembled, thereby empowering the accuracy of the extracted topics. The results, which were yielded by H-MVO for TDC, were compared against 14 well-regarded methods, involving five clustering methods, in addition to seven metaheuristic algorithms, as well as two hybrid optimization algorithms. Also, the results, which were generated by the introduced ensembled TE method, were compared against those, which were produced by five established statistical methods in the literature. As a result, the findings revealed that the suggested ensembled TE method outperformed the entire comparative methods, thereby utilizing all the external measurements for almost the entire datasets. Moreover, the new method can complement the advantages of the five previously proposed methods. Accordingly, more advanced results were obtained.

Attack Detection in IoT using Machine Learning

Article

Full-text available

Jun 2021

Many researchers have examined the risks imposed by the Internet of Things (IoT) devices on big companies and smart towns. Due to the high adoption of IoT, their character, inherent mobility, and standardization limitations, smart mechanisms, capable of automatically detecting suspicious movement on IoT devices connected to the local networks are needed. With the increase of IoT devices connected through internet, the capacity of web traffic increased. Due to this change, attack detection through common methods and old data processing techniques is now obsolete. Detection of attacks in IoT and detecting malicious traffic in the early stages is a very challenging problem due to the increase in the size of network traffic. In this paper, a framework is recommended for the detection of malicious network traffic. The framework uses three popular classification-based malicious network traffic detection methods, namely Support Vector Machine (SVM), Gradient Boosted Decision Trees (GBDT), and Random Forest (RF), with RF supervised machine learning algorithm achieving far better accuracy (85.34%). The dataset NSL KDD was used in the recommended framework and the performances in terms of training, predicting time, specificity, and accuracy were compared.

Probability distribution of intersymbol distances in random symbolic sequences: Applications to improving detection of keywords in texts and of amino acid clustering in proteins

Article

Full-text available

Nov 2016
PRE

Symbolic sequences have been extensively investigated in the past few years within the framework of statistical physics. Paradigmatic examples of such sequences are written texts, and deoxyribonucleic acid (DNA) and protein sequences. In these examples, the spatial distribution of a given symbol (a word, a DNA motif, an amino acid) is a key property usually related to the symbol importance in the sequence: The more uneven and far from random the symbol distribution, the higher the relevance of the symbol to the sequence. Thus, many techniques of analysis measure in some way the deviation of the symbol spatial distribution with respect to the random expectation. The problem is then to know the spatial distribution corresponding to randomness, which is typically considered to be either the geometric or the exponential distribution. However, these distributions are only valid for very large symbolic sequences and for many occurrences of the analyzed symbol. Here, we obtain analytically the exact, randomly expected spatial distribution valid for any sequence length and any symbol frequency, and we study its main properties. The knowledge of the distribution allows us to define a measure able to properly quantify the deviation from randomness of the symbol distribution, especially for short sequences and low symbol frequency. We apply the measure to the problem of keyword detection in written texts and to study amino acid clustering in protein sequences. In texts, we show how the results improve with respect to previous methods when short texts are analyzed. In proteins, which are typically short, we show how the measure quantifies unambiguously the amino acid clustering and characterize its spatial distribution.

Automatic Keyword Extraction on Twitter

Article

Full-text available

Jan 2015

In this paper, we build a corpus of tweets from Twitter annotated with keywords using crowdsourcing methods. We identify key differences between this domain and the work performed on other domains, such as news, which makes existing approaches for automatic keyword extraction not generalize well on Twitter datasets. These datasets include the small amount of content in each tweet, the frequent usage of lexical variants and the high variance of the cardinality of keywords present in each tweet. We propose methods for addressing these issues, which leads to solid improvements on this dataset for this task.

An Overview of Graph-Based Keyword Extraction Methods and Approaches

Article

Full-text available

Jul 2015

The paper surveys methods and approaches for the task of keyword extraction. The systematic review of methods was gathered which resulted in a comprehensive review of existing approaches. Work related to keyword extraction is elaborated for supervised and unsupervised methods, with a special emphasis on graph-based methods. Various graph-based methods are analyzed and compared. The paper provides guidelines for future research plans and encourages the development of new graph-based approaches for keyword extraction.

HESDK: A Hybrid Approach to Extracting Scientific Domain Knowledge Entities

Conference Paper

Jun 2017

Neuroscience: Big brain, big data

Article

Jan 2017

Esther Landhuis

Neuroscientists are starting to share and integrate data [mdash] but shifting to a team approach isn't easy.

Topic Detection in Twitter Using Topology Data Analysis

Conference Paper

Jun 2015

The massive volume of content generated by social media greatly exceeds human capacity to manually process this data in order to identify topics of interest. As a solution, various automated topic detection approaches have been proposed, most of which are based on document clustering and burst detection. These approaches normally represent textual features in standard n-dimensional Euclidean metric spaces. However, in these cases, directly filtering noisy documents is challenging for topic detection. Instead we propose Topol, a topic detection method based on Topology Data Analysis (TDA) that transforms the Euclidean feature space into a topological space where the shapes of noisy irrelevant documents are much easier to distinguish from topically-relevant documents. This topological space is organised in a network according to the connectivity of the points, i.e. the documents, and by only filtering based on the size of the connected components we obtain competitive results compared to other state of the art topic detection methods.

Graph-Based Methods for Clustering Topics of Interest in Twitter

Conference Paper

Jun 2015

Online Social Media provides real-time information about events and news in the physical world. A challenging problem is then to identify in a timely manner the few relevant bits of information in these massive and fast-paced streams. Most of the current topic clustering and event detection methods focus on user generated content, hence they are sensible to language, writing style and are usually expensive to compute. Instead, our approach focuses on mining the structure of the graph generated by the interactions between users. Our hypothesis is that bursts in user interest for particular topics and events are reflected by corresponding changes in the structure of the discussion dynamics. We show that our method is capable of effectively identifying event topics in Twitter ground truth data, while offering better overall performance than a purely content-based method based on LDA topic models.

Micro-blog Keyword Extraction Method Based on Graph Model and Semantic Space

Article

Oct 2013
J Multimed

There have been many domain-specific keyword extraction researches, but micro-blog- oriented keyword extraction is just beginning. This paper researches into the keyword extraction from Chinese micro-blog. Taking the characteristics of micro-blog into account, such as short, topic divergence, etc., we propose a Chinese micro-blog keyword extraction method based on the combination of multi features. Firstly create the graph model based on the co-occurrence between words, get a kind of weight based on the created graph model. The weight based on the graph model is sometimes same. In order to solve this problem, this method secondly proposes to create the semantic space based on the topic detection method, and get the statistical weight based on the semantic space. Finally, we take the location of words into account during the extraction, which is proved to be a very effective feature. Experimental results show that the proposed keyword extraction method is very successful.

Text feature classification approach for effective information extraction via discriminative sequence analysis

Article

Jan 2015

For modeling different datasets, classification is used in many existing works. However, a very few study related to terrorist tracking in sensitive area is found in the literature. To support effective detection and identification of terrorist and their activities, modeling GPS data and RFID data is necessary. But, poor representation of features leads to low classification accuracy. In this study, a classification method is studied for analyzing the behavior of terrorist in sensitive area, based on location, order of the visit, usage pattern of devices, and other activities. This approach deals with finding relevant sequences of information that are considered as good features, satisfying order-preserving property. Furthermore, to gain better computational efficiency, the length of the task-sequence is confined to a maximum length. Unlike closed sequence, the partial or length based sequence can significantly improve computational efficiency with no loss of accuracy. Proposed framework for relevant, partial sequence–based classification gives better accuracy than other approaches in real dataset.

A method for extracting task-oriented information from biological text sources

Article

Jan 2015

A method for information extraction which processes the unstructured data from document collection has been introduced. A dynamic programming technique adopted to find relevant genes from sequences which are longest and accurate is used for finding matching sequences and identifying effects of various factors. The proposed method could handle complex information sequences which give different meanings in different situations, eliminating irrelevant information. The text contents were pre-processed using a general-purpose method and were applied with entity tagging component. The bottom-up scanning of key-value pairs improves content finding to generate relevant sequences to the testing task. This paper highlights context-based extraction method for extracting food safety information, which is identified from articles, guideline documents and laboratory results. The graphical disease model verifies weak component through utilisation of development data set. This improves the accuracy of information retrieval in biological text analysis and reporting applications.