Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network

By Manoochehr Ghiassi, J. Skinner, David Zimbra. 2013

501 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets (2018, background): “These techniques include artificial neural networks (ANN) [21, 22], fuzzy logic (FL) [23], support vectormachines (SVM) [24, 25], particle swarm optimization (PSO) [26], and many others.”
A survey of the applications of text mining in financial domain (2016, methodology): “…den Poel (2015) Facebook LR, RF,NN, SVM, Adaptive Boosting AUC, Accuracy He et al. (2013) Tweets/Facebook SPSS/ Nivea Tools Satisfy/Not (Accuracy) Ghiassi et al. (2013) Tweets N-gram + ANN Accuracy Ikeda et al. (2013) Tweets Hybrid (SVM + Clustering) Recall, Precision and F-measure Thorleuchter…”
Twitter Sentiment Analysis (2015, unknown): “The results also show that larger lexicons may yield a decrease in performance due to ambiguity of words polarity and increased model complexity (agreeing with (Ghiassi et al., 2013)).”
Like It or Not (2016, methodology): “Another interesting hybrid method was presented by Ghiassi et al. [2013], who combined dynamic artiﬁcial neural network with n-gram.”
Cyber Hate Classification: 'Othering' Language And Paragraph Embedding (2018, methodology): “n) have been used to improve the accuracy of hate speech classification [14] [?] by capturing context within a sentence that is lost in the BoW model [3,7,28,41].”
Text mining for market prediction: A systematic review (2014, background): “Gradojevic and Gençay (2013) present a rare piece of research that uses fuzzy logic to improve technical trading signals successfully on the EUR-USD exchange rates but fuzzy logic is rarely used in the reviewed works for marker prediction based on text mining.”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, methodology): “S# Tasks and applications #Articles References 1 Subjectivity Classification 6 [44], [75], [110], [163], [167], [174] 2 Polarity determination 43 [12], [26], [29], [32], [33], [35], [40], [45], [48], [50], [54], [57], [66], [85], [95], [96], [108], [109], [112], [114], [123], [126], [154], [156], [1”
A Double-Layer Neural Network Framework for High-Frequency Forecasting (2017, background): “There are extensive literatures on different NN structures, such as fuzzy NN [Ghiassi et al. 2013], partially connected NN [Chang et al.”
Interpreting Neural Networks to Improve Politeness Comprehension (2016, methodology): “Substantial previous work has employed machine learning models for other sentiment analysis style tasks (Pang et al., 2002; Pang and Lee, 2004; Kennedy and Inkpen, 2006; Go et al., 2009; Ghiassi et al., 2013).”