An analysis of sentiments on facebook during the 2016 U.S. presidential election

Alashri, Saud; Kandala, Srinivasa Srivatsav; Bajaj, Vikash; Ravi, Roopek; Smith, Kendra L.; Desouza, Kevin C.

doi:10.1109/asonam.2016.7752329

Cited by 30 publications

(23 citation statements)

References 14 publications

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“…Wong et al [14] combined convex optimization techniques with SentiStrength, 2 a lexicon-based sentiment analysis tool, for modeling the political behaviors of users by analyzing tweets and retweets. Alashri et al [15] analyzed Facebook posts about the 2016 US presidential election with CoreNLP 3 [16], one of the most popular tool for natural language processing, to calculate a score for each political candidate.…”

Section: Related Workmentioning

confidence: 99%

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Learning Political Polarization on Social Media Using Neural Networks

et al. 2020

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Social media analysis is a fast growing research area aimed at extracting useful information from social media platforms. This paper presents a methodology, called IOM-NN (Iterative Opinion Mining using Neural Networks), for discovering the polarization of social media users during election campaigns characterized by the competition of political factions. The methodology uses an automatic incremental procedure based on feed-forward neural networks for analyzing the posts published by social media users. Starting from a limited set of classification rules, created from a small subset of hashtags that are notoriously in favor of specific factions, the methodology iteratively generates new classification rules. Such rules are then used to determine the polarization of people towards a faction. The methodology has been assessed on two case studies that analyze the polarization of a large number of Twitter users during the 2018 Italian general election and 2016 US presidential election. The achieved results are very close to the real ones and more accurate than the average of the opinion polls, revealing the high accuracy and effectiveness of the proposed approach. Moreover, our approach has been compared to the most relevant techniques used in the literature (sentiment analysis with NLP, adaptive sentiment analysis, emoji-and hashtag-based polarization) by achieving the best accuracy in estimating the polarization of social media users. INDEX TERMS Social media analysis, opinion mining, user polarization, neural networks, sentiment analysis, political events.

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Section: Related Workmentioning

confidence: 99%

“…1) Sentiment analysis with NLP [3], [15]. For each post, we used CoreNLP [16] for calculating a sentiment score that ranges from 0 (very negative) to 4 (very positive).…”

Section: Case Studiesmentioning

confidence: 99%

Learning Political Polarization on Social Media Using Neural Networks

et al. 2020

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“…Twitter (Rajani et al, 2014) and news articles (Mele and Crestani, 2017). Alashri et al (2016) studied Facebook posts using LDA. which published by the candidates of U.S 2016 Presidential Election to identify the significant events.…”

Section: Statistical Based Methodsmentioning

confidence: 99%

A Survey on Event Detection Models for Text Data Streams

Al-Dyani¹,

Ahmad²,

Kamaruddin³

2020

Journal of Computer Science

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Event Detection (ED) is a study area that attracts the attention of decision-makers from various disciplines in order to help them in taking the right decision. ED has been examined on various text streams like Twitter, Facebook, Emails, Blogs, Web Forums and newswires. Many ED models have been proposed in literature. In general, ED model consists of six main phases: Data collection, pre-processing, feature selection, event detection, performance evaluation and result representation. Among these phases, event detection phase has a vital rule in the performance of the ED model. Consequently, numerous supervised, unsupervised, semi-supervised detection methods have been introduced for this phase. However, unsupervised methods have been extensively utilized as ED process is considered as unsupervised task. Hence, such methods need to be categorized on such a way so it can help researchers to understand and identified the limitations lay in these methods. In this survey, ED models for text data from various Social Network sites (SNs) are analyzed based on domain type, detection methods, type of detection task. In addition, main categories for unsupervised detection methods are explicitly mentioned with revising their related works. Moreover, the major open challenges faced by researchers for building ED models are explained and discussed in detail. The main objective of this survey paper is to provide a complete view of the recent developments in ED field. Hence, help scholars to identify the limitations of existing ED models for text data and help them to recognize the interesting future works directions.

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“…In addition, the governments analyze the feelings of the public about trending topics like elections and their policies. A recent case is the prediction of the 2016 USA presidential election [16]. Furthermore, sentiment analysis can be used to enhance the capability of recommendation systems where users' interests can be identified [17,18].…”

Section: Application Of Sentiment Analysismentioning

confidence: 99%

“…• Sanders Twitter sentiment dataset 16) [129]. This dataset comprises tweets about Google, Twitter, Apple and Microsoft products.…”

Section: Hybrid Deep Neural Networkmentioning

confidence: 99%