Geolocation prediction in social media data using text analysis: A review

Utomo, Muhammad Nur Yasir; Adji, Teguh Bharata; Ardiyanto, Igi

doi:10.1109/icoiact.2018.8350674

Cited by 21 publications

(13 citation statements)

References 18 publications

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“…First, students can use several tools for online learning, there are even students who use all three, namely Google Classroom, Edmodo and WhatsApp. Second, surprisingly WhatsApp, which is an instant messaging app-based social media [29] and is not intended specifically for learning app, is apparently quite widely used to convey distance learning. These results indicate that the distance teaching process in Indonesia is very flexible.…”

Section: Pt-2: Tools Used Instead Of Universities' E-learningmentioning

confidence: 99%

Tools and Strategy for Distance Learning to Respond COVID-19 Pandemic in Indonesia

Utomo¹,

Sudaryanto²,

Saddhono³

2020

ISI

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Distance learning is a learning process that allows students and instructors are in different physical locations. During the COVID-19 pandemic, various countries including Indonesia chose the distance learning option to avoid the spread of the pandemic. However, the implementation of distance learning is not without problems. In Indonesia, the problems to implement distance learning are enormous both in terms of equitable distribution of infrastructure and costs for accessing the internet. To overcome these problems, research about best distance learning tools and its implementation strategy is really needed. Therefore, this research aimed to analyze the strategies and tools for implementing distance learning that reliable and cost-effective for the student at the same time. Based on research that has been conducted by analyzing questionnaire surveys of 256 students in Indonesia, this research recommends tools combination strategy consists of Google Classroom tool as a learning app, Whatsapp group as broadcast messaging and Zoom as a video conference media for distance learning. This research also recommended to maximize the elearning/learning app features for theoretical lessons and consider video conference just in practical lessons so that the distance learning costs become cheaper for students.

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Section: Pt-2: Tools Used Instead Of Universities' E-learningmentioning

confidence: 99%

Tools and Strategy for Distance Learning to Respond COVID-19 Pandemic in Indonesia

Utomo¹,

Sudaryanto²,

Saddhono³

2020

ISI

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“…The overall objective of the two case studies is to evaluate the similarity between the sentiment of location-based tweets reflected in election results. Geolocation information from social media data is essential for conducting geolocation-based analyzes such as traffic analysis and tourism analysis [45]. This paper by [45] reviewed geolocation prediction approaches based on text analysis in social media data.…”

Section: Related Workmentioning

confidence: 99%

“…Geolocation information from social media data is essential for conducting geolocation-based analyzes such as traffic analysis and tourism analysis [45]. This paper by [45] reviewed geolocation prediction approaches based on text analysis in social media data. Their review result shows that geolocation prediction approaches can be categorized into two categories called Content-based Geolocation Prediction and User-profiling-based Geolocation Prediction.…”

Section: Related Workmentioning

confidence: 99%

Location-based Sentiment Analysis of 2019 Nigeria Presidential Election Using A Voting Ensemble Approach

Onyenwe

Nwagbo

Orji

et al. 2022

Preprint

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Sentiment analysis is a natural language processing (NLP) tool that uses automatic methods to capture the opinions of the masses over social media such as Twitter, especially for election monitoring and predictions. However, recent researches have explored mining of public sentiment in tweets neglecting using tweets location features and ensemble learning of NLP. In this paper, we use 2019 Nigeria presidential election tweets to perform sentiment analysis through the application of a voting ensemble approach (VEA) in which the predictions from multiple techniques are combined to find the best polarity of a tweet (sentence). This is to determine public views on the 2019 Nigeria Presidential elections and compare them with actual election results. Our sentiment analysis experiment is focused on location-based viewpoints using Twitter location features. For this experiment, we live-streamed Nigeria 2019 election tweets via Twitter API to create tweets dataset of 583816 size, pre-processed the data, and applied VEA by utilizing three different Sentiment Classifiers to obtain the choicest polarity of a given tweet. Furthermore, we segmented our tweets dataset into Nigerian states and geopolitical zones, then plotted state-wise and geopolitical-wise user sentiments towards the major candidates (Buhari and Atiku) and their political parties. The overall objective of the use of states/geopolitical zones is to evaluate the similarity between the sentiment of location-based tweets compared to actual election results. The results reveal that whereas there are election outcomes that coincide with the sentiment expressed on Twitter social media in most cases as shown by the polarity scores of different locations, there are also some election results where our location analysis similarity test failed.

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“…Geotagging is the important activity to generate POI data and many researchers worked in this domain [36][37][38][39]. Some authors suggested the techniques to inference the geographical location based on text, image and categories [40][41][42][43][44][45][46][47]. Authors explained that even incomplete data can also be useful hence we divided the dataset into four classes and proposed a method to categorize the POI in a suitable class [48].…”

Section: Review Summarymentioning

confidence: 99%

Fine-tuned Predictive Model for Verifying POI Data

Monika¹,

Bundele²,

Bothale³

et al. 2021

IJACSA

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Mapping websites and geo portals are playing a vital role in daily life due to the availability of geo-tagged data. From booking a cab to search a place, getting traffic information, review of the place, searching for a doctor or best school available in the locality, we are heavily dependent on the map services and geo portals available for finding such information. There is voluminous data available on these sources and it is getting increasing every moment. These data are majorly collected through crowdsourcing methods where people are contributing. As a basic principle of Garbage in garbage out, the quality of this data impacts the quality of the services based on this data. Therefore, it is highly desired to have a model which can predict the quality/accuracy of the geotagged Point of interest data. We propose a novel Fine-Tuned Predictive Model to check the accuracy of this data using the best suitable supervised machine learning approach. This work focuses on the complete life cycle of the model building, starting from the data collection to the fine-tuning of the hyperparameters. We covered the challenges particularly to the geotagged POI data and remedies to resolve the issues to make it suitable for predictive modeling for classifying the data based on their accuracy. This is a unique work that considers multiple sources including ground truth data to verify the geotagged data using a machine learning approach. After exhaustive experiments, we obtained the best values for hyperparameters for the selected predictive model built on the real data set prepared specifically to target the proposed solution. This work provides a way to develop a robust pipeline for predicting the accuracy of crowdsourced geotagged data.

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Geolocation prediction in social media data using text analysis: A review

Cited by 21 publications

References 18 publications

Tools and Strategy for Distance Learning to Respond COVID-19 Pandemic in Indonesia

Tools and Strategy for Distance Learning to Respond COVID-19 Pandemic in Indonesia

Location-based Sentiment Analysis of 2019 Nigeria Presidential Election Using A Voting Ensemble Approach

Fine-tuned Predictive Model for Verifying POI Data

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