Transfer Learning Using Twitter Data for Improving Sentiment Classification of Turkish Political News

Automatic detection of satire in Twitter: A psycholinguistic-based approach

“…The J48 and BayesNet curves are, meanwhile, closer to the diagonal, which indicates that these classifiers are less accurate. The results obtained match those reported by other works in literature, which have concluded that SVM is one of the best machine learning classification algorithms and outperforms algorithms such as J48, BayesNet, and MaxEnt, among others [42] [43][44] [45]. Furthermore, these evaluation results are justified by the analysis of several classifiers reported in [57], in which it has been clearly demonstrated that SVM models are more robust and accurate than other algorithms, including those compared in this work.…”

SemEval-2017 Task 4: Sentiment Analysis in Twitter

“…While the tweet classification task is concerned with whether a specific tweet expresses a given sentiment towards a topic, the tweet quantification task looks at estimating the distribution of tweets about a given topic across the different sentiment classes. Most (if not all) tweet sentiment classification studies within political science (Borge-Holthoefer et al, 2015;Kaya et al, 2013;Marchetti-Bowick and Chambers, 2012), economics (Bollen et al, 2011;O'Connor et al, 2010), social science (Dodds et al, 2011), and market research (Burton and Soboleva, 2011;Qureshi et al, 2013), study Twitter with an interest in aggregate statistics about sentiment and are not interested in the sentiment expressed in individual tweets. We should also note that quantification is not a mere byproduct of classification, as it can be addressed using different approaches and it also needs different evaluation measures (Forman, 2008;Esuli and Sebastiani, 2015).…”

Semantic Sentiment Analysis of Twitter Data

“…Rather, they look at estimating the prevalence of positive and negative tweets about a given topic in a set of tweets from some time interval. Most (if not all) tweet sentiment classification studies conducted within political science [6,23,30], economics [5,41], social science [13], and market research [7,51] use Twitter with an interest in aggregate data and not in individual classifications. Thus, some tasks, such as SemEval-2016 Task 4 [38], replace classification with class prevalence estimation, which is also known as quantification in data mining and related fields.…”

“…Sentiment analysis on Twitter has applications in a number of areas, including political science [6,23,30], economics [5,41], social science [13], and market research [7,51]. It is used to study company reputation online [51], to measure customer satisfaction, to identify detractors and promoters, to forecast market growth [5], to predict the future income from newly-released movies, to forecast the outcome of upcoming elections [30,41], to study political polarization [6,68], etc.…”