Twitter Sentiment Analysis Using an Ensemble Majority Vote Classifier

Abbas, Alaa Khudhair; Salih, Ali Khalil; Hussein, Harith A.; Hussein, Qasim Mohammed; Abdulwahhab, Saba Alaa

doi:10.35741/issn.0258-2724.55.1.9

Cited by 18 publications

(13 citation statements)

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“…They found that due to factors such as the brevity of tweets, Twitter-specific language [ 30 ], and a class imbalance [ 31 ], classification algorithms achieved an accuracy of around 70%. However, Adwan et al [ 32 ] also reviewed a large number of techniques and they found a mix of accuracy scores, with some papers passing 80% accuracy while others still perform below 80% even with new algorithms [ 33 ]. Among those who have improved their accuracy, some only focus on specific politics-related data sets [ 34 ], some propose methods that require a large number of steps [ 35 ], while others address the issues with tweets, such as Twitter-specific language [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

Monselise¹,

Chang²,

Ferreira³

et al. 2021

J Med Internet Res

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Background As a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter. Objective The goal of this research was to understand public sentiment toward COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of 60 days when the vaccines were started in the United States. Using the combination of topic detection and sentiment analysis, we identified different types of concerns regarding vaccines that were expressed by different groups of the public on social media. Methods To better understand public sentiment, we collected tweets for exactly 60 days starting from December 16, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified by nonnegative matrix factorization, and emotional content was identified using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis library as well as by using sentence bidirectional encoder representations from transformer embeddings and comparing the embedding to different emotions using cosine similarity. Results After removing all duplicates and retweets, 7,948,886 tweets were collected during the 60-day time period. Topic modeling resulted in 50 topics; of those, we selected 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines were some of the major concerns of the public. Additionally, we classified the tweets in each topic into 1 of the 5 emotions and found fear to be the leading emotion in the tweets, followed by joy. Conclusions This research focused not only on negative emotions that may have led to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we were able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful for developing plans for disseminating authoritative health information and for better communication to build understanding and trust.

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Section: Introductionmentioning

confidence: 99%

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

Monselise¹,

Chang²,

Ferreira³

et al. 2021

J Med Internet Res

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“…Most models did not consider the diverse features of users. For example, Abbas et al 23 used four classifiers of Naive Bayes, Decision Tree, Multi-Layer Perception and Logistic Regression to integrate a majority voting classifier to identify the negative or positive sentiment of tweets. Vashishtha and Susan 24 used one new unsupervised system based on nine fuzzy rules to calculate and classify the sentiment of social media posts.…”

Section: Related Workmentioning

confidence: 99%

An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors

Wang

Zhu

et al. 2021

International Journal of Distributed Sensor Networks

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“Social sensors” refer to those who provide opinions through electronic communication channels such as social networks. There are two major issues in current models of sentiment analysis in social sensor networks. First, most existing models only analyzed the sentiment within the text but did not analyze the users, which led to the experimental results difficult to explain. Second, few studies extract the specific opinions of users. Only analyzing the emotional tendencies or aspect-level emotions of social users brings difficulties to the analysis of the opinion evolution in public emergencies. To resolve these issues, we propose an explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors. Our model extracts the specific opinions of the user groups on the topics and fully considers the impacts of their diverse features on sentiment analysis. We conduct experiments on 51,853 tweets about the “COVID-19” collected from 1 May 2020 to 9 July 2020. We build users’ portraits from three aspects: attribute features, interest features, and emotional features. Six machine learning algorithms are used to predict emotional tendency based on users’ portraits. We analyze the influence of users’ features on the sentiment. The prediction accuracy of our model is 64.88%.

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“…In our model, NLP will interpret the text from the OCR phase to give a better understanding of whether the image is Spam or Ham. After pre-processing the extracted text from the OCR phase to get a clean text dataset, NLP uses the bag-of-words [20] method to generate a feature vector to use in the next phase; training the model.…”

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Image Spam Detection Using Machine Learning and Natural Language Processing

Yaseen¹,

Abbas²,

Sana³

2020

Journal of Southwest Jiaotong University

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Today, images are a part of communication between people. However, images are being used to share information by hiding and embedding messages within it, and images that are received through social media or emails can contain harmful content that users are not able to see and therefore not aware of. This paper presents a model for detecting spam on images. The model is a combination of optical character recognition, natural language processing, and the machine learning algorithm. Optical character recognition extracts the text from images, and natural language processing uses linguistics capabilities to detect and classify the language, to distinguish between normal text and slang language. The features for selected images are then extracted using the bag-of-words model, and the machine learning algorithm is run to detect any kind of spam that may be on it. Finally, the model can predict whether or not the image contains any harmful content. The results show that the proposed method using a combination of the machine learning algorithm, optical character recognition, and natural language processing provides high detection accuracy compared to using machine learning alone.

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Twitter Sentiment Analysis Using an Ensemble Majority Vote Classifier

Cited by 18 publications

References 0 publications

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors

Image Spam Detection Using Machine Learning and Natural Language Processing

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