The added value of auxiliary data in sentiment analysis of Facebook posts

Meire, Matthijs; Ballings, Michel; Poel, Dirk Van den

doi:10.1016/j.dss.2016.06.013

Cited by 51 publications

(15 citation statements)

References 44 publications

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“…Lexicon based methods utilize a dictionary of words and their sentiment values -most often positive and negative -to assign a sentiment score to an input text [25,96], whereas machine learning approaches classify documents into sentiment categories based on training data [72]. Some recent studies combine the two by using lexicon scores as input for a classifier [61].…”

Section: Related Work In Other Disciplinesmentioning

confidence: 99%

Emotions Trump Facts: The Role of Emotions in on Social Media: A Literature Review

Hyvärinen¹,

Beck

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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Section: Related Work In Other Disciplinesmentioning

confidence: 99%

Emotions Trump Facts: The Role of Emotions in on Social Media: A Literature Review

Hyvärinen¹,

Beck

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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“…In linear regression, this is represented by the coefficients of the regression model, where the coefficient x 1 encapsulates the effect of x 1 on y while leaving all other variables constant. In data mining, this relationship can be graphically displayed by partial dependence plots (Meire, Ballings, & den Poel, 2016). Partial dependence plots depict the relationship between predictor and response after controlling for the average effect of all other predictors (Friedman & Meulman, 2003).…”

Section: Methodsmentioning

confidence: 99%

Predicting Self‐declared Movie Watching Behavior Using Facebook Data and Information‐Fusion Sensitivity Analysis

et al. 2019

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The main purpose of this paper is to evaluate the feasibility of predicting whether yes or no a Facebook user has self‐reported to have watched a given movie genre. Therefore, we apply a data analytical framework that (1) builds and evaluates several predictive models explaining self‐declared movie watching behavior, and (2) provides insight into the importance of the predictors and their relationship with self‐reported movie watching behavior. For the first outcome, we benchmark several algorithms (logistic regression, random forest, adaptive boosting, rotation forest, and naive Bayes) and evaluate their performance using the area under the receiver operating characteristic curve. For the second outcome, we evaluate variable importance and build partial dependence plots using information‐fusion sensitivity analysis for different movie genres. To gather the data, we developed a custom native Facebook app. We resampled our dataset to make it representative of the general Facebook population with respect to age and gender. The results indicate that adaptive boosting outperforms all other algorithms. Time‐ and frequency‐based variables related to media (movies, videos, and music) consumption constitute the list of top variables. To the best of our knowledge, this study is the first to fit predictive models of self‐reported movie watching behavior and provide insights into the relationships that govern these models. Our models can be used as a decision tool for movie producers to target potential movie‐watchers and market their movies more efficiently.

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“…SA models, methods, and techniques have been successfully applied in the context of text analytics; in applications such as the analysis of reviews on products and services [128,144]; in the analysis of social media posts in Twitter [145,146], Facebook [147], Instagram [148], etc. ; in the detection of social spam to prevent normal users from being unfairly overwhelmed with unwanted or fake content via social media [149]; in the detection or irony, sarcasm, and satire in formal and informal text [150,151]; in the detection of sexism, racism, bullying, harassment, and hate speech [152][153][154][155][156]; in influence and reputation analysis [157,158]; in political [159,160], social [161,162], and economic analysis [163,164]; in security monitoring [165]; and in health and well-being analyses [166,167].…”

Section: Sentiment Analysis As a Base Component For Text Analyticsmentioning

confidence: 99%

Sentiment Analysis for Fake News Detection

et al. 2021

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In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.

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The added value of auxiliary data in sentiment analysis of Facebook posts

Cited by 51 publications

References 44 publications

Emotions Trump Facts: The Role of Emotions in on Social Media: A Literature Review

Emotions Trump Facts: The Role of Emotions in on Social Media: A Literature Review

Predicting Self‐declared Movie Watching Behavior Using Facebook Data and Information‐Fusion Sensitivity Analysis

Sentiment Analysis for Fake News Detection

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