The emergence of social media data and sentiment analysis in election prediction

Chauhan, Priyavrat; Sharma, Nonita; Sikka, Geeta

doi:10.1007/s12652-020-02423-y

Cited by 109 publications

(48 citation statements)

References 109 publications

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“…A recent development in sentiment analysis and affective computing is to explore textual data to get public views on financial markets [3], politics [4], education [5,6], etc., just to name a few. Various research studies have also discussed the people's reactions to events expressed in social media, in general, and Twitter in particular.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Polarity Trend Amidst the Coronavirus Crisis in Peoples’ Attitudes toward the Vaccination Drive

et al. 2021

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It has been more than a year since the coronavirus (COVID-19) engulfed the whole world, disturbing the daily routine, bringing down the economies, and killing two million people across the globe at the time of writing. The pandemic brought the world together to a joint effort to find a cure and work toward developing a vaccine. Much to the anticipation, the first batch of vaccines started rolling out by the end of 2020, and many countries began the vaccination drive early on while others still waiting in anticipation for a successful trial. Social media, meanwhile, was bombarded with all sorts of both positive and negative stories of the development and the evolving coronavirus situation. Many people were looking forward to the vaccines, while others were cautious about the side-effects and the conspiracy theories resulting in mixed emotions. This study explores users’ tweets concerning the COVID-19 vaccine and the sentiments expressed on Twitter. It tries to evaluate the polarity trend and a shift since the start of the coronavirus to the vaccination drive across six countries. The findings suggest that people of neighboring countries have shown quite a similar attitude regarding the vaccination in contrast to their different reactions to the coronavirus outbreak.

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Section: Related Workmentioning

confidence: 99%

Evaluating Polarity Trend Amidst the Coronavirus Crisis in Peoples’ Attitudes toward the Vaccination Drive

et al. 2021

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“…Regarding social networks applications, the most common application of sentiment analysis is to monitor the reputation of a specific brand on Twitter or Facebook [7] and explore the reaction of people given a crisis; e.g., COVID-19 [8]. Another important application domain is in politics [9], where sentiment analysis can be useful for the election campaigns of candidates running for political positions.…”

Section: Overview Of Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

et al. 2021

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In the last decade, sentiment analysis has been widely applied in many domains, including business, social networks and education. Particularly in the education domain, where dealing with and processing students’ opinions is a complicated task due to the nature of the language used by students and the large volume of information, the application of sentiment analysis is growing yet remains challenging. Several literature reviews reveal the state of the application of sentiment analysis in this domain from different perspectives and contexts. However, the body of literature is lacking a review that systematically classifies the research and results of the application of natural language processing (NLP), deep learning (DL), and machine learning (ML) solutions for sentiment analysis in the education domain. In this article, we present the results of a systematic mapping study to structure the published information available. We used a stepwise PRISMA framework to guide the search process and searched for studies conducted between 2015 and 2020 in the electronic research databases of the scientific literature. We identified 92 relevant studies out of 612 that were initially found on the sentiment analysis of students’ feedback in learning platform environments. The mapping results showed that, despite the identified challenges, the field is rapidly growing, especially regarding the application of DL, which is the most recent trend. We identified various aspects that need to be considered in order to contribute to the maturity of research and development in the field. Among these aspects, we highlighted the need of having structured datasets, standardized solutions and increased focus on emotional expression and detection.

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“…Chauhan et al [9] in 2020 surveyed election prediction on online platforms such as Twitter and Facebook. eir study presents an in-depth analysis of the evaluation of SA techniques used in election prediction.…”

Section: Related Workmentioning

confidence: 99%

“…All these studies presented short reviews except for [9]. Besides, all the aforementioned studies performed Adhoc literature surveys, and none of them followed a detailed systematic protocol.…”

Section: Related Workmentioning

confidence: 99%

“…Many researchers have analyzed and predicted different countries' elections on different social media platforms such as Facebook and Twitter [4][5][6][7][8]. Few studies surveyed this topic [9][10][11]. To the best of our knowledge, no study ever has reported a systematic mapping study (SMS) or systematic literature review (SLR) about election predictions on Twitter.…”

Section: Introductionmentioning

confidence: 99%

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Election Prediction on Twitter: A Systematic Mapping Study

et al. 2021

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Context. Social media platforms such as Facebook and Twitter carry a big load of people’s opinions about politics and leaders, which makes them a good source of information for researchers to exploit different tasks that include election predictions. Objective. Identify, categorize, and present a comprehensive overview of the approaches, techniques, and tools used in election predictions on Twitter. Method. Conducted a systematic mapping study (SMS) on election predictions on Twitter and provided empirical evidence for the work published between January 2010 and January 2021. Results. This research identified 787 studies related to election predictions on Twitter. 98 primary studies were selected after defining and implementing several inclusion/exclusion criteria. The results show that most of the studies implemented sentiment analysis (SA) followed by volume-based and social network analysis (SNA) approaches. The majority of the studies employed supervised learning techniques, subsequently, lexicon-based approach SA, volume-based, and unsupervised learning. Besides this, 18 types of dictionaries were identified. Elections of 28 countries were analyzed, mainly USA (28%) and Indian (25%) elections. Furthermore, the results revealed that 50% of the primary studies used English tweets. The demographic data showed that academic organizations and conference venues are the most active. Conclusion. The evolution of the work published in the past 11 years shows that most of the studies employed SA. The implementation of SNA techniques is lower as compared to SA. Appropriate political labelled datasets are not available, especially in languages other than English. Deep learning needs to be employed in this domain to get better predictions.

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The emergence of social media data and sentiment analysis in election prediction

Cited by 109 publications

References 109 publications

Evaluating Polarity Trend Amidst the Coronavirus Crisis in Peoples’ Attitudes toward the Vaccination Drive

Evaluating Polarity Trend Amidst the Coronavirus Crisis in Peoples’ Attitudes toward the Vaccination Drive

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

Election Prediction on Twitter: A Systematic Mapping Study

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