Sentic patterns: Dependency-based rules for concept-level sentiment analysis

By Soujanya Poria, Erik Cambria, Grégoire Winterstein, G. Huang. Knowledge-Based Systems, 2014

354 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b527205a2c7b3555a9590a7a7bfcffe13526832c

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In Artificial Intelligence

Citations in the corpus

A survey on classification techniques for opinion mining and sentiment analysis (2017, methodology): “…(2011) 64 Li and Liu (2012) 77 Pang and Lee (2004) Penalver-Martinez et al. (2014) 89.6 Fernández-Gavilanes et al. (2016) 69.95 Fersini et al. (2016) 81.7 Saleh et al. (2011) 85.35 Boiy and Moens (2009) 87.40 Blitzer et al. (2007) Xia et al. (2016) 80 Xia et al. (2011) 85.58 Poria et al. (2014) 87”
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, background): “duced the concept of modeling sentiment composition, learned via heuristics and rules (Choi & Cardie, 2008), hybrid systems (Rentoumi et al., 2010), syntactic dependencies (Nakagawa et al., 2010; Poria et al., 2014; Hutto & Gilbert, 2014), amongst others. Sentiment Lexicons are at the heart ”
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “[117] proposed a new framework combining computational intelligence, linguistics and common-sense computing [118].”
Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges (2023, methodology): “A concept-level sentiment analysis framework was reported by Poria et al. [62] on the movie review and the Blitzer dataset.”
A survey on sentiment analysis methods, applications, and challenges (2022, background): “A well-known example is a SentiWordNet lexicon. contrarily, statistical processes are entirely automated and are widely used for feature selection, although they typically fail to distinguish between sentimental and non-sentimental features (Poria et al. 2014; Varelas et al. 2005).”
Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis (2015, methodology): “We will continue our study of reasoning over text (Jimenez et al., 2015; Pakray et al., 2011; Sidorov et al., 2014; Sidorov, 2014) and in particular of conceptbased sentiment analysis (Poria et al., 2014b).”
Tensor Fusion Network for Multimodal Sentiment Analysis (2017, methodology): “Various approaches have been proposed to model sentiment from language, including methods that focus on opinionated words (Hu and Liu, 2004; Taboada et al., 2011; Poria et al., 2014b; Cambria et al., 2016), n-grams and language models (Yang and Cardie, 2012), sentiment compositionality and dependenc”
Fusing audio, visual and textual clues for sentiment analysis from multimodal content (2016, neutral): “They thus offer strong potential as a viable alternative technique for large-scale computing and machine learning in many different application fields, including image [6], text [7], and speech [8] processing, as well as multimodal data analysis [9].”
Modelling input texts: from Tree Kernels to Deep Learning (2015, background): “While a few successful attempts have been made to use more involved linguistic analysis for opinion mining, such as dependency trees [116,159] and constituency trees with vectorized nodes [150], recently, a comprehensive study by Wang and Manning [176] showed that a simple model using bigrams and SV”
A Rule-Based Approach to Aspect Extraction from Product Reviews (2014, background): “…recent developments in common-sense reasoning (Cambria et al., 2011; Cambria et al., 2014a) and concept-level sentiment analysis (Xia et al., 2013; Poria et al., 2014) to ﬁrst obtain the dependency structure of each sentence and, hence, exploit external knowledge to extract aspects and infer the…”
Distilling Neural Networks for Greener and Faster Dependency Parsing (2020, background): “The syntactic information attained from parsers has been shown to benefit a number of other NLP tasks such as relation extraction (Zhang et al., 2018), machine translation (Chen et al., 2018), and sentiment analysis (Poria et al., 2014; Vilares et al., 2017).”
Trends in extreme learning machines: A review (2015, background): “…al. (2012), Suresh et al. (2009), Wang, Huang, et al. (2011), Zhou et al. (2013), Text classification and understanding Cambria et al. (in press), Poria et al. (2014), Yang and Mao (2013), Zhao et al. (2011), Zheng et al. (2013), System modeling and prediction Balbay, Kaya, et al. (2012),…”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, background): “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”