Political Sentiment Analysis Using Twitter Data

Elghazaly, Tarek; Mahmoud, Amal; Hefny, Hesham A.

doi:10.1145/2896387.2896396

Cited by 60 publications

(26 citation statements)

References 11 publications

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“…Preprocessing [17] Normalization, POS tagging [24][25][26][27] Stemming [28][29][30][31][32][33] Text cleaning [34][35][36][37][38][39] Normalization, stemming, stop words removal [40][41][42] Text cleaning, normalization, stemming, stop words removal [43][44][45] Normalization Text cleaning, normalization, tokenization, stemming, stop words removal [49][50][51][52] Normalization, tokenization [53,54] Text cleaning, normalization, tokenization [55,56] Normalization, tokenization, POS tagging [13,[57][58][59][60][61][62][63][64] Normalization, tokenization, stemming, stop words removal [65,66] Normalization, tokenization, stemming, lemmatization [67,68] Text cleaning, normalization, tokenization, stemming [69] Text cleaning, tokenization, stemming, negation detection [70]…”

Section: Referencementioning

confidence: 99%

“…Most studies focused on ASA applications in a limited set of domains, such as politics [15,48,62,89], hotel [79,113], business and economy [12,20,129], arts and books [29,32,92], entertainment and movies [71,73,99], and sport [81,96] Several papers were published [12,74,83] to study ASA for several purposes such as building Arabic senti-lexicon, designing a framework for ASA, and comparing two free online SA tools that support Arabic. ese studies involved collecting small datasets with size less than 3000 tweets that are relevant to several domains such as education, sports, and politics.…”

Section: Arabic Sentiment Analysis Applicationsmentioning

confidence: 99%

“…ere are 22 studies in which NB accuracy outperforms SVM [52,62,83,106,118]. NB has the ability to classify sentiments using a small training set.…”

Section: Machine Learning Approachmentioning

confidence: 99%

See 2 more Smart Citations

Arabic Sentiment Analysis: A Systematic Literature Review

Ghallab

Mohsen

Ali

2020

Applied Computational Intelligence and Soft Computing

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With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). This paper introduces a systematic review of the existing literature relevant to ASA. The main goals of the review are to support research, to propose further areas for future studies in ASA, and to smoothen the progress of other researchers’ search for related studies. The findings of the review propose a taxonomy for sentiment classification methods. Furthermore, the limitations of existing approaches are highlighted in the preprocessing step, feature generation, and sentiment classification methods. Some likely trends for future research with ASA are suggested in both practical and theoretical aspects.

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Section: Referencementioning

confidence: 99%

Section: Arabic Sentiment Analysis Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Arabic Sentiment Analysis: A Systematic Literature Review

Ghallab

Mohsen

Ali

2020

Applied Computational Intelligence and Soft Computing

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“…Sentiment analysis is to extract and quantify subjective information including the status of attitudes, emotions and opinions from a variety of contents such as texts, images and audios [47]. Sentiment analysis has been drawing great attentions because of its wide applications in business and government intelligence, political science, sociology and psychology [2,3,13,33]. From a technical perspective, textual sentiment analysis is first explored by researchers as an NLP task.…”

Section: Related Work 21 Sentiment Analysismentioning

confidence: 99%

Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

Chen

Yuan

You

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

134

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Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis. We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments.

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“…The results showed that among the classifiers, the SVM classifier achieved the best average accuracy, followed by NB, DT and finally BPNN. Elghazaly et al [17] evaluated the use of two classifiers, SVM and NB, on the SA of Egyptian political election tweets. The tweets were represented using BoW features with a TF-IDF weighting scheme.…”

Section: Related Workmentioning

confidence: 99%