A sentiment analysis model of Agritech startup on Facebook comments using naive Bayes classifier

Kewsuwun, Nawapon; Kajornkasirat, Siriwan

doi:10.11591/ijece.v12i3.pp2829-2838

Cited by 27 publications

(22 citation statements)

References 19 publications

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“…To validate the output of the tweet labeling done by the VADER pretrained model [44], we used five machine learning classifiers including NB [45], LR [46], [47], SVMs [47], [48], DT [49], and KNN [50]. The reason we chose these classifiers is because they have been successfully used to classify tweets in [42].…”

Section: F Tweet Sentiment Classificationmentioning

confidence: 99%

Vaccine Hesitancy Hotspots in Africa: An Insight From Geotagged Twitter Posts

Ogbuokiri

Ahmadi

Nia

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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Many social media users express concerns about vaccines and their side effects on Twitter. These concerns lead to a compromise of confidence which brings about vaccine hesitancy. In Africa, vaccine hesitancy is a major challenge faced by health policymakers in the fight against COVID-19. Given that most tweets are geotagged, clustering them according to their sentiments could help identify locations that may likely experience vaccine hesitancy for health policy and planning. In this study, we collected 70 000 geotagged vaccine-related tweets in nine African countries, from December 2020 to February 2022. The tweets were classified into three sentiment classespositive, negative, and neutral. The quality of the classification outputs was achieved using Naíve Bayes (NB), logistic regression (LR), support vector machines (SVMs), decision tree (DT), and K-nearest neighbor (KNN) machine learning classifiers. The LR achieved the highest accuracy of 71% with an average area under the curve of 85%. The point-based location technique was used to calculate the hotspots based on the locations of the classified tweets. Locations with green, red, and gray backgrounds on the map signify a hotspot for positive, negative, and neutral sentiments. The outcome of this research shows that discussions on social media can be analyzed to identify hotspots during a disease outbreak, which could inform health policy in planning and management of vaccine hesitancy in Africa.

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Section: F Tweet Sentiment Classificationmentioning

confidence: 99%

Vaccine Hesitancy Hotspots in Africa: An Insight From Geotagged Twitter Posts

Ogbuokiri

Ahmadi

Nia

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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“…The dataset was divided into two parts, namely, training set (0.80) and test set (0.20) which was fitted into five different machine learning classification algorithms for evaluation and prediction. The algorithms are Naive Bayes (NB) (34), Logistic Regression (LR) (35,36), Support Vector Machines (SVM) (36, 37), Decision Tree (DT) (38), and K-Nearest Neighbors (KNN) (39). We chose these algorithms because they have been tested to perform well with classification and prediction with text based dataset.…”

Section: Figurementioning

confidence: 99%

Public sentiments toward COVID-19 vaccines in South African cities: An analysis of Twitter posts

Ogbuokiri

Ahmadi

Bragazzi

et al. 2022

Front. Public Health

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Amidst the COVID-19 vaccination, Twitter is one of the most popular platforms for discussions about the COVID-19 vaccination. These types of discussions most times lead to a compromise of public confidence toward the vaccine. The text-based data generated by these discussions are used by researchers to extract topics and perform sentiment analysis at the provincial, country, or continent level without considering the local communities. The aim of this study is to use clustered geo-tagged Twitter posts to inform city-level variations in sentiments toward COVID-19 vaccine-related topics in the three largest South African cities (Cape Town, Durban, and Johannesburg). VADER, an NLP pre-trained model was used to label the Twitter posts according to their sentiments with their associated intensity scores. The outputs were validated using NB (0.68), LR (0.75), SVMs (0.70), DT (0.62), and KNN (0.56) machine learning classification algorithms. The number of new COVID-19 cases significantly positively correlated with the number of Tweets in South Africa (Corr = 0.462, P < 0.001). Out of the 10 topics identified from the tweets using the LDA model, two were about the COVID-19 vaccines: uptake and supply, respectively. The intensity of the sentiment score for the two topics was associated with the total number of vaccines administered in South Africa (P < 0.001). Discussions regarding the two topics showed higher intensity scores for the neutral sentiment class (P = 0.015) than for other sentiment classes. Additionally, the intensity of the discussions on the two topics was associated with the total number of vaccines administered, new cases, deaths, and recoveries across the three cities (P < 0.001). The sentiment score for the most discussed topic, vaccine uptake, differed across the three cities, with (P = 0.003), (P = 0.002), and (P < 0.001) for positive, negative, and neutral sentiments classes, respectively. The outcome of this research showed that clustered geo-tagged Twitter posts can be used to better analyse the dynamics in sentiments toward community–based infectious diseases-related discussions, such as COVID-19, Malaria, or Monkeypox. This can provide additional city-level information to health policy in planning and decision-making regarding vaccine hesitancy for future outbreaks.

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“…To validate the output of the tweet labelling done by the VADER pre-trained model [31], we used five machine learning classifiers including Naive Bayes (NB) [32], Logistic Regression (LR) [33], [34], Support Vector Machines (SVMs) [34], [35], Decision Tree (DT) [36], and K-Nearest Neighbours (KNN) [37]. The reason we chose these classifiers is because they have been successfully used to classify tweets in [30] and [38].…”

Section: E Tweet Sentiment Classificationmentioning

confidence: 99%

Vaccine Hesitancy Hotspots in Africa: An Insight from Geotagged Twitter Posts

Ogbuokiri¹,

Ahmadi²,

Nia³

et al. 2022

Preprint

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In this research, we collected 70,000 COVID–19 vaccine-related geotagged Twitter posts from nine African countries. The duration is from December 2020 to February 2022. We used VADER to classify the tweets into three sentiment classes (positive, negative, and neutral). The outputs were validated using machine learning classification algorithms, including, Naive Bayes, Logistic Regression, Support Vector Machines, Decision Tree, and K-Nearest Neighbour. We identified hotspots by clustering the sentiment of these tweets using the point-based location technique. These hotspots were visualised on the map using ArcGIS Online. On the map, we used green to represent positive sentiment dominance, red to represent negative sentiment dominance, and grey to represent neutral sentiment dominance. The outcome of this research shows that social media data can be used to complement existing data in identifying hotspots during future outbreaks, especially in the areas where there is little or no available data. It can also be used to inform health policy in managing vaccine hesitancy.

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A sentiment analysis model of Agritech startup on Facebook comments using naive Bayes classifier

Cited by 27 publications

References 19 publications

Vaccine Hesitancy Hotspots in Africa: An Insight From Geotagged Twitter Posts

Vaccine Hesitancy Hotspots in Africa: An Insight From Geotagged Twitter Posts

Public sentiments toward COVID-19 vaccines in South African cities: An analysis of Twitter posts

Vaccine Hesitancy Hotspots in Africa: An Insight from Geotagged Twitter Posts

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