Sentiments Analysis On Public Land Transport Infrastructure in Davao Region using Machine Learning Algorithms

Buladaco, Mark Van M.

doi:10.30534/ijatcse/2020/97912020

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…If an enterprise adapts this proposal, there will be greater returns in customer retention, income, and sustainability. As a recommendation also, it would be best if sentiments analysis will be done from social media posts similar to the study of Buadaco et al where they scrape a micro blog social media platform to analyze the sentiments and emotions on land transport infrastructure in the Philippines [7].…”

Section: Discussionmentioning

confidence: 99%

A Proposed Ideal Network Design for Collaborative Workspace Businesses

2021

IJATCSE

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Collaborative workspace businesses are now a trend in developing countries such as the Philippines. Coworking space is a business service provision model that lets individuals and teams work independently or collaboratively in a shared office space. In the Philippines, there is a big trend in starting a coworking space business. The millennial generation, which comprises the bulk of the metropolitan sector, is the main reason behind coworking space growth. Computer networking can contribute to the improvement of sales and customer retention and service of coworking space businesses. Implementing managed network services will reduce operational, maintenance, service, hardware, software, and infrastructure costs. Managed network services can enable the coworking space business to focus on its primary business goals In this paper, an ideal network design for collaborative workspace businesses was developed. The building floor layout is essential for the managed networks' design. It was determined that a two-story floor layout is ideal for the company and for implementing the proposed network design. Analysis of ideal network management was proposed in administration, operation, security, and maintenance. Detailed logical and physical network designs with ideal network equipment and technologies were created to improve the coworking space business's services by implementing managed networks.

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Section: Discussionmentioning

confidence: 99%

A Proposed Ideal Network Design for Collaborative Workspace Businesses

2021

IJATCSE

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“…Based on literature studies, most of the previous studies were only related to one SDGs point. SDGs 1 [14], [15]; SDGs 2 [16], [17]; SDGs 3 [4], [18], [19], [20], [21], [22]; SDGs 4 [6], [23], [24], [25], [26]; SDGs 5 [27], [28]; SDGs 7 [29], [30]; SDGs 8 [3], [8], [31], [32]; SDGs 9 [33]; SDGs 10 [34], [35]; SDGs 11. [36]; SDGs 12 [37], [38]; SDGs 13 [39], [40]; SDGs 14 [41], [42]; SDGs 16 [43]; and SDGs 17 [44].…”

Section: Methodsmentioning

confidence: 99%

Implementation of Ensemble Machine Learning Classifier and Synthetic Minority Oversampling Technique for Sentiment Analysis of Sustainable Development Goals in Indonesia

Gufroni,

Hoeronis,

Fajar

et al. 2024

JOIV : Int. J. Inform. Visualization

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As part of the Sustainable Development Goals (SDGs), governments worldwide have committed to improving people's lives to improve the quality of life for all, including the 17 such goals that were agreed upon in 2015 to benefit the human race as a whole. It would be interesting to see how society responds to the SDGs after approximately half of them have been achieved. This public response was analyzed in terms of sentiment. Within the total number of internet users in Indonesia, there are 18.45 million Twitter users. The platform enables anyone to write about anything they are experiencing in their lives, such as what is happening in their environment, what is happening in their education system, what is happening in the food industry, how people feel, and many more. The platform enables anyone to write about anything they are experiencing in their lives, such as what is happening in their environment, what is happening in their education system, what is happening in the food industry, how people feel, and many more. To model the data collected, the researchers used Ensemble Machine Learning Classifiers (EMLC) to model the data by using a machine learning classifier that uses machine learning techniques. The best model in this study is EMLC-Stacking with a data splitting of 80:20 and using SMOTE, which obtains an accuracy of 91%. This accuracy results from a 5% increase compared to when not using SMOTE. From 15,698 tweets, this research found that 47% were positive sentiments, 28% were negative sentiments, and 25% were neutral sentiments. The results that we measured offer hope that there will be a positive trend in the journey of the SDGs until 2030 if these findings are true.

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“…Data splitting into training and testing data is needed to validate at the end. In this study, data splitting was carried out using a ratio of 75:25 for training data and testing data [46]. Where the training data is used to be trained through oversampling and machine learning models, then the model's results based on the training data will be validated by data testing so that the validation results can measure how effective the proposed architecture is in overcoming the sentiment analysis problem.…”

Section: Data Splittingmentioning

confidence: 99%

Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia

Kristiyanti,

Sanjaya,

Tjokro

et al. 2024

IJ-AI

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<span lang="EN-US">Global recession news dominates social media, particularly in Indonesia, with social news platforms on Twitter generating public responses and <br /> re-tweetings on the issue. Mining these opinions from Twitter using a sentiment analysis approach yields invaluable insights. The research stages included data collection, pre-processing, data labeling using the <br /> lexical-based method like valence aware dictionary and sentiment reasoner (VADER) and TextBlob, sampling techniques using synthetic minority oversampling technique (SMOTE) and random over sampling (ROS) before and after splitting data, and modeling using machine learning such as support vector machines (SVM), k-nearest neighbour (KNN), naive Bayes, and model evaluation. The problem is that almost 300,000 data collected from NodeXL are unbalanced. The findings show that models with balanced datasets show better model evaluation results. The sampling technique was carried out before and after splitting the data. The model evaluation results show that the Bernoulli-naive Bayes algorithm, with the VADER labeling technique, and the SMOTE sampling technique after splitting data, obtains the best accuracy of 84%, and using the ROS technique obtains an accuracy of 81%. On the other hand, with the SMOTE and ROS technique before splitting data on the SVM algorithm, it gets the best accuracy of 93% from before if only using SVM only reached 84%.</span>

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Sentiments Analysis On Public Land Transport Infrastructure in Davao Region using Machine Learning Algorithms

Cited by 6 publications

References 14 publications

A Proposed Ideal Network Design for Collaborative Workspace Businesses

A Proposed Ideal Network Design for Collaborative Workspace Businesses

Implementation of Ensemble Machine Learning Classifier and Synthetic Minority Oversampling Technique for Sentiment Analysis of Sustainable Development Goals in Indonesia

Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia

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