Classification of Traffic Events Notified in Social Networks' Texts

2019 ◽  
pp. 342-355 ◽  
Author(s):  
Ana Maria Magdalena Saldana-Perez ◽  
Marco Antonio Moreno-Ibarra ◽  
Miguel Jesus Torres-Ruiz
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Learning Algorithm ◽  
Relevant Information ◽  
User Generated Content ◽  
Machine Learning Algorithm ◽  
Information Processes ◽  
The Social ◽  
Language Text

It is interesting to exploit the user-generated content (UGC) and to use it with a view to infer new data; volunteered geographic information (VGI) is a concept derived from UGC, whose main importance lies in its continuously updated data. The present approach tries to explode the use of VGI by collecting data from a social network and a RSS service; the short texts collected from the social network are written in Spanish language; text mining and a recovery information processes are applied over the data in order to remove special characters on text and to extract relevant information about the traffic events on the study area; then data are geocoded. The texts are classified by using a machine learning algorithm into five classes, each of them represents a specific traffic event or situation.

Classification of Traffic Events Notified in Social Networks' Texts

2018 ◽  
pp. 6973-6984 ◽  
Author(s):  
Ana Maria Magdalena Saldana-Perez ◽  
Marco Antonio Moreno-Ibarra ◽  
Miguel Jesus Torres-Ruiz
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Learning Algorithm ◽  
Relevant Information ◽  
User Generated Content ◽  
Machine Learning Algorithm ◽  
Information Processes ◽  
The Social

It is interesting to exploit the user generated content (UGC), and to use it with a view to infer new data; volunteered geographic information (VGI) is a concept derived from UGC, which main importance lies in its continuously updated data. The present approach tries to explode the use of VGI, by collecting data from a social network and a RSS service; the short texts collected from the social network are written in Spanish language; a text mining and a recovery information processes are applied over the data, in order to remove special characters on text, and to extract relevant information about the traffic events on the study area, then data are geocoded. The texts are classified by using a machine learning algorithm into five classes, each of them represents a specific traffic event or situation.

Understanding the expression of grievances in the Arabic Twitter-sphere using machine learning

2019 ◽  
Vol 5 (2) ◽  
pp. 108-119
Author(s):  
Yeslam Al-Saggaf ◽  
Amanda Davies
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Network ◽  
Network Analysis ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Content Type ◽  
The Social ◽  
Twitter Users

Purpose The purpose of this paper is to discuss the design, application and findings of a case study in which the application of a machine learning algorithm is utilised to identify the grievances in Twitter in an Arabian context. Design/methodology/approach To understand the characteristics of the Twitter users who expressed the identified grievances, data mining techniques and social network analysis were utilised. The study extracted a total of 23,363 tweets and these were stored as a data set. The machine learning algorithm applied to this data set was followed by utilising a data mining process to explore the characteristics of the Twitter feed users. The network of the users was mapped and the individual level of interactivity and network density were calculated. Findings The machine learning algorithm revealed 12 themes all of which were underpinned by the coalition of Arab countries blockade of Qatar. The data mining analysis revealed that the tweets could be clustered in three clusters, the main cluster included users with a large number of followers and friends but who did not mention other users in their tweets. The social network analysis revealed that whilst a large proportion of users engaged in direct messages with others, the network ties between them were not registered as strong. Practical implications Borum (2011) notes that invoking grievances is the first step in the radicalisation process. It is hoped that by understanding these grievances, the study will shed light on what radical groups could invoke to win the sympathy of aggrieved people. Originality/value In combination, the machine learning algorithm offered insights into the grievances expressed within the tweets in an Arabian context. The data mining and the social network analyses revealed the characteristics of the Twitter users highlighting identifying and managing early intervention of radicalisation.

scholarly journals CLASSIFICATION OF TRAFFIC RELATED SHORT TEXTS TO ANALYSE ROAD PROBLEMS IN URBAN AREAS

2017 ◽  
Vol XLII-4/W3 ◽  
pp. 91-97 ◽  
Author(s):  
A. M. M. Saldana-Perez ◽  
M. Moreno-Ibarra ◽  
M. Tores-Ruiz
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Urban Dynamics ◽  
Volunteer Geographic Information ◽  
Car Accidents ◽  
The Social

The Volunteer Geographic Information (VGI) can be used to understand the urban dynamics. In the <i>classification of traffic related short texts to analyze road problems in urban areas</i>, a VGI data analysis is done over a social media’s publications, in order to classify traffic events at big cities that modify the movement of vehicles and people through the roads, such as car accidents, traffic and closures. The classification of traffic events described in short texts is done by applying a supervised machine learning algorithm. In the approach users are considered as sensors which describe their surroundings and provide their geographic position at the social network. The posts are treated by a text mining process and classified into five groups. Finally, the classified events are grouped in a data corpus and geo-visualized in the study area, to detect the places with more vehicular problems.

scholarly journals Multi-Class Assessment Based on Random Forests

2021 ◽  
Vol 11 (3) ◽  
pp. 92
Author(s):  
Mehdi Berriri ◽  
Sofiane Djema ◽  
Gaëtan Rey ◽  
Christel Dartigues-Pallez
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Random Forests ◽  
Learning Algorithm ◽  
Teaching Staff ◽  
Machine Learning Algorithm ◽  
Process Data ◽  
Training Courses ◽  
Education Courses

Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to their profile. The second objective is to allow the teaching staff to propose training courses adapted to students by anticipating their possible difficulties. This is possible thanks to a machine learning algorithm called Random Forest, allowing for the classification of the students depending on their results. We had to process data, generate models using our algorithm, and cross the results obtained to have a better final prediction. We tested our method on different use cases, from two classes to five classes. These sets of classes represent the different intervals with an average ranging from 0 to 20. Thus, an accuracy of 75% was achieved with a set of five classes and up to 85% for sets of two and three classes.

An Analysis of Machine Learning Algorithm for the Classification of Emotion Recognition

2021 ◽  
pp. 399-408
Author(s):  
Aditi Sakalle ◽  
Pradeep Tomar ◽  
Harshit Bhardwaj ◽  
Divya Acharya ◽  
Arpit Bhardwaj
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

scholarly journals Image Classification using CNN and Machine Learning

2019 ◽  
pp. 575-580
Author(s):  
G. Keerthi Devipriya ◽  
E. Chandana ◽  
B. Prathyusha ◽  
T. Seshu Chakravarthy
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Training Models ◽  
Classification Of Images ◽  
Respective Category

Here by in this paper we are interested for classification of Images and Recognition. We expose the performance of training models by using a classifier algorithm and an API that contains set of images where we need to compare the uploaded image with the set of images available in the data set that we have taken. After identifying its respective category the image need to be placed in it. In order to classify images we are using a machine learning algorithm that comparing and placing the images.

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