Tweets mining for French Presidential Election

Węgrzyn-Wolska, Katarzyna; Bougueroua, Lamine

doi:10.1109/cason.2012.6412392

Cited by 15 publications

(8 citation statements)

References 15 publications

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“…Compared with movie reviews and news articles, tweets have a lot of difference [7]. On the one hand, tweets are shorter and more ambiguous than movie reviews and news articles because of the limitation of words.…”

Section: Introductionmentioning

confidence: 99%

Explore the Effects of Emoticons on Twitter Sentiment Analysis

Węgrzyn-Wolska¹,

Bougueroua²,

Yu³

et al. 2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Section: Introductionmentioning

confidence: 99%

Explore the Effects of Emoticons on Twitter Sentiment Analysis

Węgrzyn-Wolska¹,

Bougueroua²,

Yu³

et al. 2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

Self Cite

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“…A framework for detecting and tracking political abuse was described by [19]. Wegrzyn-wolska, [26] has conducted a content analysis of social network by analyzing the intensity and polarity of opinion and developed a system that collects, evaluate and rate tweets automatically for trend evaluation using Twitter dataset related to French presidential election. De Groot, [5] implemented a sentiment analysis tool that classifies tweets related to popular mobile phones and their brands.…”

Section: Related Workmentioning

confidence: 99%

“…This data mostly contain vital information which can be used nationally and internationally for business purposes and decision making. For social media users, social interaction has turn out to be an indispensable part of their lives [26]. Therefore, it is easier to get their opinion from their social interaction on social media.…”

Section: Introductionmentioning

confidence: 99%

“…The International Journal of E-Learning and Educational Technologies in the Digital Media (IJEETDM) 3(1): [23][24][25][26][27][28][29][30] The Society of Digital Information and Wireless Communications (SDIWC), 2017 ISSN: 2410-0439 (Online) different source of data might sometimes require different modeling process.…”

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confidence: 99%

“…The International Journal of E-Learning and Educational Technologies in the Digital Media (IJEETDM) 3(1): [23][24][25][26][27][28][29][30] The …”

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A Comparative Analysis of the Performance of Three Machine Learning Algorithms for Tweets on Nigerian dataset

Tabra¹

2017

IJEETDM

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The popularity of Twitter as a social media platform is increasingly becoming very important, this has no doubt impact positively on the business, social and political aspect of our lives, and hence, the need for researchers to focus on Twitter opinion miming became crucial. Despite the increase in the number of Twitter users in Nigeria generating a huge amount of data from the discussions on national issues became a challenge; limited research has been carried out on Twitter data sources from Nigerian context. The objective of this paper is to use Twitter data from Nigeria to carry out a comparative analysis of the performance criteria based on accuracy, precision and recall on three classification algorithms. The purpose of which is to find out the best algorithm that fits the dataset. Dataset used was gathered using Twitter Application Programming Interface (API) given a trending hash tag on national issues. This dataset was preprocessed before modeling. Twitter opinion mining classification algorithms: Naïve Bayse, Support Vector Machine (SVM) and Maximum Entropy were chosen, modeled and evaluated. Findings from this research show that dataset from Nigeria can be used to mine the opinions of citizens on national issues; it also shows that SVM was disappointing using the dataset, while Naïve Bayse classifier outperform others with accuracy of 83%; this is implying that the best among the three models can classify tweets wrongly with the rate of 17%. The best model (Naïve Bayse) was implemented as Software; the Software downloads and automatically classifies tweets into three opinions namely positive, negative and neutral.

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Perceptions of presidential candidates' personalities in twitter

Bhattacharya

Yang

Srinivasan

et al. 2015

Asso for Info Science & Tech

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Political sentiment analysis using social media, especially Twitter, has attracted wide interest in recent years. In such research, opinions about politicians are typically divided into positive, negative, or neutral. In our research, the goal is to mine political opinion from social media at a higher resolution by assessing statements of opinion related to the personality traits of politicians; this is an angle that has not yet been considered in social media research. A second goal is to contribute a novel retrieval-based approach for tracking public perception of personality using Gough and Heilbrun's Adjective Check List (ACL) of 110 terms describing key traits. This is in contrast to the typical lexical and machine-learning approaches used in sentiment analysis. High-precision search templates developed from the ACL were run on an 18-month span of Twitter posts mentioning Obama and Romney and these retrieved more than half a million tweets. For example, the results indicated that Romney was perceived as more of an achiever and Obama was perceived as somewhat more friendly. The traits were also aggregated into 14 broad personality dimensions. For example, Obama rated far higher than Romney on the Moderation dimension and lower on the Machiavellianism dimension. The temporal variability of such perceptions was explored.

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Tweets mining for French Presidential Election

Cited by 15 publications

References 15 publications

Explore the Effects of Emoticons on Twitter Sentiment Analysis

Explore the Effects of Emoticons on Twitter Sentiment Analysis

A Comparative Analysis of the Performance of Three Machine Learning Algorithms for Tweets on Nigerian dataset

Perceptions of presidential candidates' personalities in twitter

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