Twitter chirps for Syrian people: Sentiment analysis of tweets related to Syria Chemical Attack

Bashir, Saimah; Bano, Shohar; Shueb, Sheikh; Gul, Sumeer; Mir, Aasif Ahmad; Ashraf, Romisa; Shakeela,; Noor, Neelofar

doi:10.1016/j.ijdrr.2021.102397

Cited by 35 publications

(20 citation statements)

References 26 publications

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“…Even Gul et al (2018) reveal that “time heals everything, and an event, no matter how people are connected to it, begins to fade from their memory”. They also witnessed a heavy traffic of tweets related to the Kashmir floods on Twitter in the initial days, but a decline is observed in the number of tweets with every passing day.” Another study on tweets related to “Syria Chemical Attack” , confirms that with the passing days the discussion traffic over Twitter decreases (Bashir et al , 2021). The results showcase that most of the tweets express “positive” sentiments, showing that the public remains hopeful and positive about COVID-19 vaccines.…”

Section: Resultsmentioning

confidence: 84%

“…In this stage, the dataset gets normalized and prepared for the classification algorithm to run smoothly and bring effective minimum time results” (Jahanbin et al , 2019). The pre-processing techniques improve the effectiveness of tweet sentiment analysis (Bashir et al , 2021). The default data-cleaning and preprocessing techniques available in the “Orange Software” were applied to the dataset, which include transformation , tokenization , and filtering . Transformation : The technique helps to convert the raw data into a format that is machine-understandable and clean the data to remove certain discrepancies so that inconsistent data can be avoided (Neogi et al , 2021).…”

Section: Methodsmentioning

confidence: 99%

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Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati

Mir

Sevukan

Gul

2021

LHT

Self Cite

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PurposeTwitter is gaining popularity as a microblogging and social networking service to discuss various social issues. Coronavirus disease 2019 (COVID-19) has become a global pandemic and is discussed worldwide. Social media is an instant platform to deliberate various dimensions of COVID-19. The purpose of the study is to explore and analyze the public sentiments related to COVID-19 vaccines across the Twitter messages (positive, neutral, and negative) and the impact tweets make across digital social circles.Design/methodology/approachTo fetch the vaccine-related posts, a manual examination of randomly selected 500 tweets was carried out to identify the popular hashtags relevant to the vaccine conversation. It was found that the hashtags “covid19vaccine” and “coronavirusvaccine” were the two popular hashtags used to discuss the communications related to COVID-19 vaccines. 23,575 global tweets available in public domain were retrieved through “Twitter Application Programming Interface” (API), using “Orange Software”, an open-source machine learning, data visualization and data mining toolkit. The study was confined to the tweets posted in English language only. The default data cleaning and preprocessing techniques available in the “Orange Software” were applied to the dataset, which include “transformation”, “tokenization” and “filtering”. The “Valence Aware Dictionary for sEntiment Reasoning” (VADER) tool was used for classification of tweets to determine the tweet sentiments (positive, neutral and negative) as well as the degree of sentiments (compound score also known as sentiment score). To assess the influence/impact of tweets account wise (verified and unverified) and sentiment wise (positive, neutral, and negative), the retweets and likes, which offer a sort of reward or acknowledgment of tweets, were used.FindingsA gradual decline in the number of tweets over the time is observed. Majority (11,205; 47.52%) of tweets express positive sentiments, followed by neutral (7,948; 33.71%) and negative sentiments (4,422; 18.75%), respectively. The study also signifies a substantial difference between the impact of tweets tweeted by verified and unverified users. The tweets related to verified users have a higher impact both in terms of retweets (65.91%) and likes (84.62%) compared to the tweets tweeted by unverified users. Tweets expressing positive sentiments have the highest impact both in terms of likes (mean = 10.48) and retweets (mean = 3.07) compared to those that express neutral or negative sentiments.Research limitations/implicationsThe main limitation of the study is that the sentiments of the people expressed over one single social platform, that is, Twitter have been studied which cannot generalize the global public perceptions. There can be a variation in the results when the datasets from other social media platforms will be studied.Practical implicationsThe study will help to know the people's sentiments and beliefs toward the COVID-19 vaccines. Sentiments that people hold about the COVID-19 vaccines are studied, which will help health policymakers understand the polarity (positive, negative, and neutral) of the tweets and thus see the public reaction and reflect the types of information people are exposed to about vaccines. The study can aid the health sectors to intensify positive messages and eliminate negative messages for an enhanced vaccination uptake. The research can also help design more operative vaccine-advocating communication by customizing messages using the obtained knowledge from the sentiments and opinions about the vaccines.Originality/valueThe paper focuses on an essential aspect of COVID-19 vaccines and how people express themselves (positively, neutrally and negatively) on Twitter.

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Section: Resultsmentioning

confidence: 84%

Section: Methodsmentioning

confidence: 99%

Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati

Mir

Sevukan

Gul

2021

LHT

Self Cite

View full text Add to dashboard Cite

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“…Because of that data mining actually has long roots from fields of science such as artificial intelligence (artificial intelligence), machine learning, statistics and databases. Several methods that are often mentioned in the data mining literature include clustering, classification, association rules mining, neural networks, genetic algorithms and others [19].…”

Section: Data Miningmentioning

confidence: 99%

COVID-19 Vaccination: A Retrospective Observation and Sentiment Analysis of the Twitter Social Media Platform in Indonesia

Hananto

2022

IJIIS Int. J. Informatics Inf. Syst.

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“…▪ Dimensional emotion models present a few dimensions with some parameters and characterize emotions according to those dimensions. Each emotion occupies a location in this space [1][2][3][4][5]. The more representative emotion models of this approach are Russell [60] and Plutchik [61].…”

Section: ) Emotion Modelsmentioning

confidence: 99%

Combining Context-Aware Embeddings and an Attentional Deep Learning Model for Arabic Affect Analysis on Twitter

Elfaik

Nfaoui

2021

IEEE Access

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Affect analysis has recently attracted a great deal of attention due to the rapid development of online social platforms (i.e., Twitter, Facebook). Affect analysis is a part of a broader area of affective computing that aims to detect and grasp human emotions or affects within a piece of writing. Context awareness is very relevant for identifying human emotions and affects behind a piece of text. Capturing the context of a piece of text is often perceived as a challenge. In addition to the own unique features of tweets (shortness, noisiness, short length, etc.), the Arabic language is characterized by its agglutination and morphological richness. In this paper, we address the problem of Arabic affect detection (multilabel emotion classification) by combining the transformer-based model for Arabic language understanding AraBERT and an attention-based LSTM-BiLSTM deep model. AraBERT generates the contextualized embedding, and the attention-based LSTM-BiLSTM determines the label-emotion of tweets by extracting both past and future contexts considering temporal information flow in both directions. Additionally, the attention mechanism is applied to the output of LSTM-BiLSTM to emphasize different words. Our proposed approach was evaluated using the reference dataset of SemEval-2018 Task 1 (Affect in Tweets). The comprehensive results show that the proposed approach outperforms eight current state-of-the-art and baseline methods, and it achieves significant accuracy (53.82%) compared to 1 st place in SemEval2018-Task1: (Affect in Tweets) competition. In addition, our proposed model outperforms the best recently reported model in the literature, with an enhancement of 2.62% in accuracy.

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Twitter chirps for Syrian people: Sentiment analysis of tweets related to Syria Chemical Attack

Cited by 35 publications

References 26 publications

Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati

Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati

COVID-19 Vaccination: A Retrospective Observation and Sentiment Analysis of the Twitter Social Media Platform in Indonesia

Combining Context-Aware Embeddings and an Attentional Deep Learning Model for Arabic Affect Analysis on Twitter

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