NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network
            Model for Irony Detection in Twitter

Vu, Thanh; Nguyen, Dat Quoc; Vu, Xuan-Son; Nguyen, Dai Quoc; Catt, Michael; Trenell, Michael I.

doi:10.18653/v1/s18-1085

Cited by 17 publications

(3 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We extended their model by building a deep neural network model to automatically classify users' privacy-concern to high (HiPC), medium (MePC), and low (LoPC) based on given five personality trait scores and status updates. Motivated from previous studies [23,32,30], we extract the following content features from Facebook status updates, which will be used in the following deep neural network model to predict users' privacy-concern:…”

Section: Content Analysis On User Generated Contentmentioning

confidence: 99%

See 1 more Smart Citation

Generic Multilayer Network Data Analysis with the Fusion of Content and Structure

Vu,

Santra,

Chakravarthy

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Multi-feature data analysis (e.g., on Facebook, LinkedIn) is challenging especially if one wants to do it efficiently and retain the flexibility by choosing features of interest for analysis. Features (e.g., age, gender, relationship, political view etc.) can be explicitly given from datasets, but also can be derived from content (e.g., political view based on Facebook posts). Analysis from multiple perspectives is needed to understand the datasets (or subsets of it) and to infer meaningful knowledge. For example, the influence of age, location, and marital status on political views may need to be inferred separately (or in combination). In this paper, we adapt multilayer network (MLN) analysis, a nontraditional approach, to model the Facebook datasets, integrate content analysis, and conduct analysis, which is driven by a list of desired application based queries. Our experimental analysis shows the flexibility and efficiency of the proposed approach when modeling and analyzing datasets with multiple features.

show abstract

Section: Content Analysis On User Generated Contentmentioning

confidence: 99%

“…L1) Age: Any two users are connected by an edge when they both fall into a same age group, namely [≤ 20],[21][22][23][24][25][26][27][28][29][30],[31][32][33][34][35][36][37][38][39][40], [41-50], [51-60], and [≥ 61]. (L2) Gender: Any two users with the same gender are connected.…”

mentioning

confidence: 99%

Generic Multilayer Network Data Analysis with the Fusion of Content and Structure

Vu,

Santra,

Chakravarthy

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Both the polarity contrast and the surface features were combined with word embeddings and they were used as input to an ensemble soft voting classifier based on Logistic Regression and SVM paradigms. It is interesting to note that, most of the participating teams addressed the tasks by using emotional and polarity features in order to enrich their systems with the aim of explaining the irony by means of polarity contrast [25,220,221].…”

Section: Irony Detectionmentioning

confidence: 99%

Attention-based Approaches for Text Analytics in Social Media and Automatic Summarization

Barba¹

View full text Add to dashboard Cite

Nowadays, society has access, and the possibility to contribute, to large amounts of the content present on the internet, such as social networks, online newspapers, forums, blogs, or multimedia content platforms. These platforms have had, during the last years, an overwhelming impact on the daily life of individuals and organizations, becoming the predominant ways for sharing, discussing, and analyzing online content. Therefore, it is very interesting to work with these platforms, from different points of view, under the umbrella of Natural Language Processing. In this thesis, we focus on two broad areas inside this field, applied to analyze online content: text analytics in social media and automatic summarization. Neural networks are also a central topic in this thesis, where all the experimentation has been performed by using deep learning approaches, mainly based on attention mechanisms. Besides, we mostly work with the Spanish language, due to it is an interesting and underexplored language with a great interest in the research projects we participated in.On the one hand, for text analytics in social media, we focused on affective analysis tasks, including sentiment analysis and emotion detection, along with the analysis of the irony. In this regard, an approach based on Transformer Encoders, based on contextualizing pretrained Spanish word embeddings from Twitter, to address sentiment analysis and irony detection tasks, is presented. We also propose the use of evaluation metrics as loss functions, in order to train neural networks for reducing the impact of the class imbalance in multi-class and multi-label emotion detection tasks. Additionally, a specialization of BERT both for the Spanish language and the Twitter domain, that takes into account inter-sentence coherence in Twitter conversation flows, is presented. The performance of all these approaches has been tested with different corpora, from several reference evaluation benchmarks, showing very competitive results in all the tasks addressed.On the other hand, we focused on extractive summarization of news articles and TV talk shows. Regarding the summarization of news articles, a theoretical framework for extractive summarization, based on siamese hierarchical networks with attention mechanisms, is presented. Also, we present two instantiations of this framework: Siamese Hierarchical Attention Networks and Siamese Hierarchical Transformer Encoders. These systems were ResumenHoy en día, la sociedad tiene acceso y posibilidad de contribuir a grandes cantidades de contenidos presentes en Internet, como redes sociales, periódicos online, foros, blogs o plataformas de contenido multimedia. Todo este tipo de medios han tenido, durante los últimos años, un impacto abrumador en el día a día de individuos y organizaciones, siendo actualmente medios predominantes para compartir, debatir y analizar contenidos online. Por este motivo, resulta de interés trabajar sobre este tipo de plataformas, desde diferentes puntos de vista, bajo el paraguas del Procesamiento...

show abstract

Decision-Making Support Method Based on Sentiment Analysis of Objects and Binary Decision Tree Mining

Phan

Tran

Nguyen

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter

Cited by 17 publications

References 35 publications

Generic Multilayer Network Data Analysis with the Fusion of Content and Structure

Generic Multilayer Network Data Analysis with the Fusion of Content and Structure

Attention-based Approaches for Text Analytics in Social Media and Automatic Summarization

Decision-Making Support Method Based on Sentiment Analysis of Objects and Binary Decision Tree Mining

Contact Info

Product

Resources

About