Machine learning and Natural Language Processing of social media data for event detection in smart cities

Hodorog, Andrei; Petri, Ioan; Rezgui, Yacine

doi:10.1016/j.scs.2022.104026

Cited by 42 publications

(18 citation statements)

References 26 publications

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“…The ⨁ operator is a concatenation operator and is used to combine words that have been converted into vectors into a matrix form followed by the activation function of the linear unit rectifier. The feature function is written in (11).…”

Section:  Classificationmentioning

confidence: 99%

“…Before executing calculations using the Deep learning model of conversational sentence categorization. However, the preprocessing procedure must be executed to ensure that the input is first processed using natural language processing (NLP) [11]. NLP is a computerized technology that explains the function of software or hardware that analyzes spoken or written language in a computer system [12].…”

Section: Introductionmentioning

confidence: 99%

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Comparing LSTM and CNN Methods in Case Study on Public Discussion about Covid-19 in Twitter

Kurniawan¹,

Romadhoni²,

Zahrona³

et al. 2022

IJACSA

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This study compares two Deep Learning model methods, which include the Long Short-Term Memory (LSTM) method and the Convolution Neural Network (CNN) method. The aim of the comparison is to discover the performance of two different fundamental deep learning approaches which are based on convolutional theory (CNN) and deal with the vanishing gradient problem (LSTM). The purpose of this study is to compare the accuracy of the two methods using a dataset of 4169 obtained by crawling social media using the Twitter API. The Tweets data we've obtained are based on a specific hashtag keyword, namely "covid-19 pandemic". This study attempts to assess the sentiment of all tweets about the Covid-19 viral epidemic to determine whether tweets about Covid-19 contain positive or negative thoughts. Before classification, the Preprocessing and Word Embedding steps are completed, and this study has determined that the epoch used is 20 and the hidden layer is 64. Following the classification process, this study concludes that the two methods are appropriate for classifying public conversation sentences against Covid-19. According to this study, the LSTM method is superior, with an accuracy of 83.3%, a precision of 85.6%, a recall of 90.6%, and an f1-score of 88.5%. While the CNN method achieved an accuracy of 81%, precision of 71.7%, recall of 72%, and f1-score of 72%.

show abstract

Section:  Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparing LSTM and CNN Methods in Case Study on Public Discussion about Covid-19 in Twitter

Kurniawan¹,

Romadhoni²,

Zahrona³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…Machine learning techniques can leverage big data analytics to provide valuable insights for smart city planning and management Hodorog et al, 2022). By analyzing large volumes of data generated by urban sensors, social media platforms, and other sources, machine learning algorithms can identify patterns, trends, and correlations that may not be readily apparent to human observers Hodorog et al, 2022). This data-driven approach can enable evidence-based decision-making, facilitate proactive interventions, and enhance the overall intelligence of cities (Kitchin, 2014).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning (Ai) for Identifying Smart Cities

Hammoumi,

Rhinane

2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Cities worldwide are attempting to be claimed as smart, but truly classifying as such remains a great challenge. This paper aims to use artificial intelligence AI to classify the smart city's performance as well as the factors linked to it. This is based on the perceptions of residents on issues related to structures and technology applications available in their cities. To achieve this goal, the study included 200 cities worldwide. For 147 cities we captured the perceptions of 120 residents in each city, by answering a survey of 39 questions evolving around two main Pillars: ‘Structures’ that refers to the existing infrastructure of the city and the ‘Technology’ pillar that describes the technological provisions and services available to the inhabitants. And each one is evaluated under five key areas: health and safety, mobility, activities, opportunities, and governance. The final score of the other 53 cities, was measured by using the data openly available on the internet. And this by means of different algorithms of machine learning such as Random Forest RF, Artificial Neural Network ANN, Support Vector Machine (SVM), and Gradient Boost (XGB). These algorithms have been compared and evaluated in order to select the best one. The tests showed that Random Forest RF alongside with Artificial Neural Network ANN, with the highest level of accuracy, are the best trained model. This study will enable other researches to use machine learning in the identification process of smart cities.

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“…Çalışma neticesinde finansal alanda karar verme konusunda önerdikleri yöntemin başarılı bir piyasa taraması yapabileceğini ifade etmişlerdir. Hodorog ve ark., yerleşim yerlerinde olay tespiti için makine öğrenme algoritmaları ile birlikte doğal işleme yöntemini birlikte kullanmıştır [21]. Çalışmada regresyon analizi ile olaylar ile memnuniyet arasındaki ilişkiler doğrulanırken; en yüksek doğruluk oranı %88.5 ile elde edilmiştir.…”

Section: Introductionunclassified

Modelling the machines’ language with natural language processing and machine learning algorithms

DAYAN

Yilmaz

2022

DÜMF MD

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Language is one of the most important elements of communication for people and all living things. A large number of studies have been conducted in the literature on the formation of languages and natural language processing processes. It has been observed that these studies contain differences in terms of analysis, approach and method. In the study, a model has been proposed for machines to create their own language as a contribution to the literature. With this point of view, it has been contributed to the idea that the efficiency of all processes will increase by communicating with each other in their own language like humans. With the aim of developing an approach for machines to produce their own languages, it is aimed to distinguish the sounds of living things by classifying them and to generate new sounds by using convolutional deep neural network method. In the study, the applied alphabet was decided by the system. In next step, Regenerative Artificial Neural Networks were used together with Mel Frequency Cepstral Coefficient and Dynamic Time Warp methodologies to create similar sounds and the living things were named with their respective sounds. Unlike similar studies, a visual data was applied which was converted from the audio files that was obtained from Kaggle open data repository "Audio Cats and Dogs" dataset. In addition, the model was supported with CNN networks to achieve effective performance.

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Machine learning and Natural Language Processing of social media data for event detection in smart cities

Cited by 42 publications

References 26 publications

Comparing LSTM and CNN Methods in Case Study on Public Discussion about Covid-19 in Twitter

Comparing LSTM and CNN Methods in Case Study on Public Discussion about Covid-19 in Twitter

Machine Learning (Ai) for Identifying Smart Cities

Modelling the machines’ language with natural language processing and machine learning algorithms

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