Rapid Automatic Keyword Extraction Research Articles

Crash Contributing Factors and Patterns Associated with Fatal Truck-Involved Crashes in Bangladesh: Findings from the Text Mining Approach

Despite extensive research on traffic injury severities, relatively little is known about the factors contributing to truck-involved crashes in developing countries, especially in the context of Bangladesh. Because of the unavailability of authentic crash data sources, this study collected data from alternative sources such as online English news media reports. The current study prepared a database of 144 truck-involved fatal crash reports during the period of 12 months (January 2021 to December 2021). The crash reports contain a bag of 15,300 words. Several state-of-the-art text mining tools were utilized to identify crash patterns, including word cloud analysis, word frequency analysis, word co-occurrence network analysis, rapid automatic keyword extraction, and topic modeling. The analysis revealed several important crash contributing factors, such as the type of vehicle involved (auto-rickshaw, bus, van, motorcycle), the manner of collision (head-on), the time of the day (morning, night), driver behavior (speeding, overtaking, wrong-way driving), and environmental factors (dense fog). In addition, “coming from opposite direction” and “head-on collision” are two important sequences of events in truck-involved crashes. Truck drivers are also involved in crashes with trains at rail crossings. The findings of this research can assist policymakers in identifying crash avoidance strategies to lower truck-related crashes in Bangladesh.

An Artificial Intelligence based automated case-based reasoning (CBR) system for severity investigation and root-cause analysis of road accidents – Comparative analysis with the predictions of ChatGPT

Road accidents have been progressively causing havoc in our society and certain preventive measures must be taken to reduce or possibly eliminate road accidents. The derivative of a road accident ranges from a mild injury to casualty. This research work mainly focusses on developing a novel case-based reasoning system to investigate and troubleshoot the cause of road accidents on a war-foot basis. First, the dominant attributes contributing to the cause of road accidents are identified and finalized as 28. A unique road accident dataset is developed which comprises of 1028 data collected from web resources, popular news magazines and extended further to large scale database of one-million cases by biased random number simulation. Each attribute is given a severity weightage of 1,2 and 3 for computing the net weighted score for a case in the database. Also, non-weighted scores are computed by introduction of a primary number dataset to maintain the uniqueness of the score which is further used for similarity analytics. Now, an accident news is randomly selected, and Rapid automatic keyword extraction (RAKE) schema is used as Natural language processor (NLP) for extracting the dominant keywords from the news articles. The extracted keywords are compared and further mapped into a factor-matrix comprising 28 attributes causing road accidents. Further, similarity analytics is performed to evaluate the severity scores and comparison of new cases. The system demonstrated high retrieval accuracy with all road accident cases collected from real world scenarios. This research has great prospects on troubleshooting road accident cases effectively and provides instant promising troubleshooting measures to prevent such accidents in the future. Also, the proposed framework might be useful for intelligent decision-making systems and automated driving systems. Based on the final outlook, a comprehensive framework for national road safety could be developed and passed as a valid law for implementation. Finally, the forecasted results of the proposed algorithm are compared with the predictions of Chat GPT program.

Performance Evaluation of Keyword Extraction Techniques and Stop Word Lists on Speech-To-Text Corpus

The dawn of conversational user interfaces, through which humans communicate with computers through voice audio, has been reached. Therefore, Natural Language Processing (NLP) techniques are required to focus not only on text but also on audio speeches. Keyword Extraction is a technique to extract key phrases out of a document which can provide summaries of the document and be used in text classification. Existing keyword extraction techniques have commonly been used on only text/typed datasets. With the advent of text data from speech recognition engines which are less accurate than typed texts, the suitability of keyword extraction is questionable. This paper evaluates the suitability of conventional keyword extraction methods on a speech-to-text corpus. A new audio dataset for keyword extraction is collected using the World Wide Web (WWW) corpus. The performances of Rapid Automatic Keyword Extraction (RAKE) and TextRank are evaluated with different Stoplists on both the originally typed corpus and the corresponding Speech-To-Text (STT) corpus from the audio. Metrics of precision, recall, and F1 score was considered for the evaluation. From the obtained results, TextRank with the FOX Stoplist showed the highest performance on both the text and audio corpus, with F1 scores of 16.59% and 14.22%, respectively. Despite lagging behind text corpus, the recorded F1 score of the TextRank technique with audio corpus is significant enough for its adoption in audio conversation without much concern. However, the absence of punctuation during the STT affected the F1 score in all the techniques.

Framing of COVID-19 in Newspapers: A Perspective from the US-Mexico Border.

The degree to which the media report a health emergency affects the seriousness with which the people respond to combat the health crisis. Engagement from local newspapers in the US has received scant scrutiny, even though there is a sizable body of scholarship on the analysis of COVID-19 news. We fill this void by focusing on the Rio Grande Valley area of the US-Mexico border. To understand the differences, we compared such local news coverage with the coverage of a national news outlet. After collecting the relevant news articles, we used sentiment analysis, rapid automatic keyword extraction (RAKE), and co-occurrence network analysis to examine the main themes and sentiments of COVID-19 news articles. The RAKE identified that county-specific news or local regulations are more prevalent among the key terms in The Monitor which are absent in USA Today. The co-occurrence network shows the coverage of the disruption of sports season in USA Today which is not present in The Monitor. The sentiment analysis presents fear emotion is more dominant in USA Today, but trust emotion becomes more prevalent in The Monitor news coverage. These findings show us that, although the subject of the health emergency is the same, local and national newspapers describe it in different ways, and the sentiments they convey are also not the same.

News Recommender System Based on User Log History Using Rapid Automatic Keyword Extraction

There are many ways to find information; one of them is reading online news. However, searching for news online becomes more difficult because we should visit multiple platforms to find information. Sometimes, the recommended news doesn't match the user's interests. In many prior works, news recommendations are based on trending. Thus, the recommended news may not necessarily match the user's interests. To overcome this, we built a web-based news recommender system to make it easier for users to find news. We use the Rapid Automatic Keyword Extraction (RAKE) method in the recommendation process because this method can recommend news based on user preferences by utilizing user history logs. RAKE converts the title and content of the news into vector representation using Count vectorizer and applies the Cosine Similarity function to compare similarities between news. The test results show that the average performance of our proposed system is 90.8%, this accuracy outperforms earlier systems in terms of performance by the purpose of the recommender system, i.e., diversity, novelty, and relevance.

Machine learning in exploration the decoupling paradigm in transport

The article deals with one of the paradigms of sustainable development, i.e. decoupling. The article focuses on machine learning in the field of text mining on decoupling in transport. The research problem was formulated in the form of a question: does machine learning improve the process of exploring information contained in scientific publications on decoupling in transport? The article aims to explore the decoupling paradigm in transport in tasks of natural language processing with the use of machine learning. In the article, research was conducted in the field of text mining and emotion profiler (by Plutchnik emotions), using lemmatization and techniques such as word cloud, a bag of words, Latent Dirichlet Allocation, RAKE (rapid automatic keyword extraction) algorithm, and hierarchical clustering. Important keywords, phrases for the description of the decoupling paradigm, methods of its research, and words that induce increased trust in this concept were found. The authors proposed an original framework for the research procedure.

Exploring the “Dark Matter” of Social Interaction: Systematic Review of a Decade of Research in Spontaneous Interpersonal Coordination

Interpersonal coordination is a research topic that has attracted considerable attention this last decade both due to a theoretical shift from intra-individual to inter-individual processes and due to the development of new methods for recording and analyzing movements in ecological settings. Encompassing spatiotemporal behavioral matching, interpersonal coordination is considered as “social glue” due to its capacity to foster social bonding. However, the mechanisms underlying this effect are still unclear and recent findings suggest a complex picture. Goal-oriented joint action and spontaneous coordination are often conflated, making it difficult to disentangle the role of joint commitment from unconscious mutual attunement. Consequently, the goals of the present article are twofold: (1) to illustrate the rapid expansion of interpersonal coordination as a research topic and (2) to conduct a systematic review of spontaneous interpersonal coordination, summarizing its latest developments and current challenges this last decade. By applying Rapid Automatic Keyword Extraction and Latent Dirichlet Allocation algorithms, keywords were extracted from PubMed and Scopus databases revealing the large diversity of research topics associated with spontaneous interpersonal coordination. Using the same databases and the keywords “behavioral matching,” “interactional synchrony,” and “interpersonal coordination,” 1,213 articles were identified, extracted, and screened following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. A total of 19 articles were selected using the following inclusion criteria: (1) dynamic and spontaneous interactions between two unacquainted individuals (2) kinematic analyses, and (3) non-clinical and non-expert adult populations. The results of this systematic review stress the proliferation of various definitions and experimental paradigms that study perceptual and/or social influences on the emergence of spontaneous interpersonal coordination. As methods and indices used to quantify interpersonal coordination differ from one study to another, it becomes difficult to establish a coherent picture. This review highlights the need to reconsider interpersonal coordination not as the pinnacle of social interactions but as a complex dynamical process that requires cautious interpretation. An interdisciplinary approach is necessary for building bridges across scattered research fields through opening a dialogue between different theoretical frameworks and consequently provides a more ecological and holistic understanding of human social cognition.

Improved Rapid Automatic Keyword Extraction for Voice-based Mechanical Arm Control

Improved Rapid Automatic Keyword Extraction for Voice-based Mechanical Arm Control

Visualizing sharia law: an information retrieval approach based on key extraction algorithm

Islamic jurisprudence as it relates to every element of Muslim life that founds in Sharia. Many interpretations exist for Sharia, all based on differing Islamic schools of thought and multiple laws concerning Sharia. In Malaysia, there are 135 sections of Sharia law related to family and marriage in Act 303. The survey conducted proves that Malaysians struggled when looking for sharia law and took a toll of time. Therefore, we propose a web-based Sharia Law Finder (SLF) system that solves the sharia law’s information retrieval issues in Malaysia. The SLF adapted the key extraction (KE) algorithm, which is Term Frequency-Inverse Document Frequency (TF-IDF), and Rapid Automatic Keyword Extraction (RAKE). The testing for implementation of both algorithm effectiveness is using the functional and usability test. The solution enhances using visualization tools which are bubble charts and word cloud. Bubble charts visualize the related Sharia law based on users’ queries and word cloud to visualize keywords used by the users. A total of 30 respondents have tested the functionality and usability of SLF. As a result, the system successfully works as specified functionality, 96.58% for the System Usability Scale, indicating the proposed solution’s acceptance.

NER‐RAKE: An improved rapid automatic keyword extraction method for scientific literatures based on named entity recognition

AbstractThe proposing of an accurate and efficient model for automatic keyword extraction of scientific literature is conducive to promoting the development of academic text mining research, such as literature retrieval optimization, research topic discovery, topic evolution analysis, emerging trend detection and so on, with the continuous expansion of digital academic resources. A keyword extraction method is proposed called NER‐RAKE which combines Named Entity Recognition (NER) process with Rapid automatic keyword extraction (RAKE), Bidirectional Long Short‐Term Memory Network Conditional Random Field (BiLSTM‐CRF) is used to recognize the domain entities in the scientific literature so as to enrich the list of candidate keywords divided by RAKE, and the frequency threshold is set to ensure the validity of candidate keywords. NER‐RAKE is better than RAKE in accuracy and recall rate through the keyword extraction experiment in the computer science field. It is effective to combine the process of NER with RAKE, which can improve the efficiency of keyword extraction.

Semantic Unsupervised Automatic Keyphrases Extraction by Integrating Word Embedding with Clustering Methods

Increasingly, the web produces massive volumes of texts, alone or associated with images, videos, photographs, together with some metadata, indispensable for their finding and retrieval. Keywords/keyphrases that characterize the semantic content of documents should be, automatically or manually, extracted, and/or associated with them. The paper presents a novel method to address the problem of the automatic unsupervised extraction of keywords/phrases from texts, expressed both in English and in Italian. The main feature of this approach is the integration of two methods that have given interesting results: word embedding models, such as Word2Vec or GloVe able to capture the semantics of words and their context, and clustering algorithms, able to identify the essence of the terms and choose the more significant one(s), to represent the contents of a text. In the paper, the datasets used are presented, together with the method implemented and the results obtained. These results will be discussed, commented, and compared with those obtained in previous experimentations, using TextRank, Rapid Automatic Keyword Extraction (RAKE), and TF-IDF.

A Keyword-Based Literature Review Data Generating Algorithm—Analyzing a Field from Scientific Publications

A scientific review is a type of article that summarizes the current state of a specific field, which is crucial for promoting the advancement of our science community. Authors need to read hundreds of research articles to prepare the data and insights for a comprehensive review, which is time-consuming and labor-intensive. In this work, we present an algorithm that can automatically extract keywords from the meta-information of each article and generate the basic data for review articles. Two different fields—communication engineering, and lab on a chip technology—were analyzed as examples. We first built an article library by downloading all the articles from the target journal using a python-based crawler. Second, the rapid automatic keyword extraction algorithm was implemented on the title and abstract of each article. Finally, we classified all extracted keywords into class by calculating the Levenshtein distance between each of them. The results demonstrated its capability of not only finding out how communication engineering and lab on a chip were evolved in the past decades but also summarizing the analytical outcomes after data mining of the extracted keywords. Our algorithm is more than a useful tool for researchers during the preparation of a review article, it can also be applied to quantitatively analyze the past, present and help authors predict the future trend of a specific research field.

Automatic Keyword and Sentence-Based Text Summarization for Software Bug Reports

Text Summarization is a process which efficiently retrieves the relevant information from documents. The objective of the proposed, unsupervised approach is to summarize bug reports (software artefacts) with complete content and diversified information. The proposed approach utilizes Rapid Automatic Keyword Extraction and term frequency-inverse document frequency method to extract meaningful keywords and key-phrases with a relevant score. For sentence extraction, fuzzy C-means clustering is used to extracts sentences having high degree of membership from each cluster above a set threshold value. A rule-engine is used for sentence selection. The rules are generated with the domain knowledge and based on the extracted information by the keywords and sentences selected by the clustering method. Cohesive and coherent summary is generated by the proposed method on apache bug reports. For redundancy removal and to re-rank generated summary, hierarchical clustering is presented to enrich the extracted summary. The proposed approach is evaluated on newly constructed Apache project Bug Report Corpus (APBRC) and existing Bug Report Corpus (BRC). The results are compared on the basis of performance metrics such as precision, recall, pyramid precision and F-score. The experimental results depict that our proposed approach attains significant improvement over other baseline approaches such as BRC and LRCA. It also attains significant improvement over existing state-of-art unsupervised approaches such as Hurried, centroid and others. It extracts significant keyword phrases and sentences from each cluster to achieve full coverage and coherent summary. The results evaluated on APBRC corpus attains an average value of 78.22%, 82.18%, 80.10% and 81.66% for precision, recall, f-score and pyramid precision respectively.

COMMERCIALIZATION OF INNOVATIONS THROUGH INTERNATIONALIZATION: A SYSTEMATIC LITERATURE REVIEW

The main purpose of the article is to identify the state of knowledge between two multidimensional phenomena: commercialization of innovation and internationalization. In terms of research methods, a systematic literature review, based on the 91 publications located in reputable scientific databases (Scopus and Web of Science) between the years 2000–2017 and Rapid Automatic Keyword Extraction (RAKE), was applied. The main findings provide information on the concept of commercialization of innovations via internationalization, and specifically on different research perspectives and areas of analysis with division of the bibliometric data. The selection of the articles for further analysis is perceived as the main limitation of this study, as some papers may not have been included due to missing keywords or due to having been absent from the two selected databases. The article analyses theoretical research and empirical papers and enables identification of problems in the current research as well as identifying trends in the area of the studied phenomenon. The results on the topic of commercialization of innovation through internationalization have indicated that this issue is still in a premature phase but with an upward trend of research development.

Territorial innovation models: to be or not to be, that’s the question

Industrial agglomerations are key in explaining the development paths followed by territories, particularly at sub-national levels. This field of research has received increasing attention in the last decades, what has led to the emergence of a variety of models intended to characterize innovation at the regional level. Moulaert and Sekia (Reg Stud 37:289–302, 2003) introduced the concept of ‘Territorial Innovation Models’ (TIMs) as a generic name that embraced these conceptual models of regional innovation in the literature. However, the literature does not help to assess the extent to which convergence or divergence is found across TIMs. In this paper we aim to clarify if there are clear boundaries across TIMs, so each TIM has particular characteristics that make it conceptually different from others, and hence, justify its introduction in the literature. Based on natural language processing methodologies, we extract the key terms of a large volume of academic papers published in peer review journals indexed in the Web of Science for the following TIMS: industrial districts, innovative milieu, learning regions, clusters, regional innovation systems, local production systems and new industrial spaces. We resort to Rapid Automatic Keyword Extraction to identify the associations between the topics extracted from the previous corpus. Finally, a configuration to visualise the results of the methodology followed is also proposed. Our results evidence that the previous models do not have a unique flavour but are rather similar in their taste. We evidence that there is quite little that is truly new in the different TIMs in terms of theory-building and the concepts being used in each model.

Geoscience keyphrase extraction algorithm using enhanced word embedding

A large amount of unstructured textual data about geoscience structures and minerals is buried in geoscience documents and is unused. Automatic keyphrase extraction provides opportunities to leverage this wealth of data for analysis and knowledge discovery. However, keyphrase extraction remains a complicated task, and the performance of state-of-the-art approaches is still low. Automatic discovery of high-quality and meaningful keyphrases requires the application of useful knowledge and suitable techniques.Seeing both challenges and opportunities in the situation described above, this paper proposes an ontology and enhanced word embedding-based (OEWE) methodology for automatic keyphrase extraction from geoscience documents. We first develop a quantitative analysis for keyphrase extraction evaluation based on conditional probability and the naive Bayesian model, which is valuable when human-annotated keyphrases are not available. The domain ontology is then performed on a multiway tree to enrich the domain-specific knowledge on certain concepts and relationships in a domain. Simultaneously, word2vec, a model of a word distribution using deep learning, is updated by applying the geological ontology, and it links domain background information and identifies infrequent but representative keyphrases. We use two homemade geoscience datasets to evaluate the performance of OEWE. We compare our method with frequency, term frequency-inverse document frequency (TF-IDF), TextRank and rapid automatic keyword extraction (RAKE), finding that our method achieves average F1 scores of 30.1% and 40.7% on two manually annotated datasets.

Patent Keyword Extraction Algorithm Based on Distributed Representation for Patent Classification

Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction algorithms are based on discrete bag-of-words type of word representation of the text. In this paper, we propose a patent keyword extraction algorithm (PKEA) based on the distributed Skip-gram model for patent classification. We also develop a set of quantitative performance measures for keyword extraction evaluation based on information gain and cross-validation, based on Support Vector Machine (SVM) classification, which are valuable when human-annotated keywords are not available. We used a standard benchmark dataset and a homemade patent dataset to evaluate the performance of PKEA. Our patent dataset includes 2500 patents from five distinct technological fields related to autonomous cars (GPS systems, lidar systems, object recognition systems, radar systems, and vehicle control systems). We compared our method with Frequency, Term Frequency-Inverse Document Frequency (TF-IDF), TextRank and Rapid Automatic Keyword Extraction (RAKE). The experimental results show that our proposed algorithm provides a promising way to extract keywords from patent texts for patent classification.

Drug Side Effect Analyser Using Machine Learning

People are dependent on medicinal drugs on one way or the other for every simple cause such as headache, cold etc. Every drug has a negative impact on a person's body. Some people are unaware of the side effects of the drugs and they consume it without prescription. Social network platforms such as twitter provide an opportunity for people to express themselves. Using twitter as the source of data, this paper aims to find the side effects of drugs with the help of machine learning algorithms.SVM (Support Vector Machine) algorithm is used for drug related classification with an accuracy of 75%.Sentiment analysis is performed using VADER (Valence Aware Dictionary for sEntiment Reasoning) to handle negations, conjunctions and question marks present in the tweets. Keyword Extraction is performed using RAKE (Rapid Automatic Keyword Extraction) to get the side effects. Keywords: -- Machine learning, Sentiment Analysis, Natural Language Processing, RAKE, SVM