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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 56 1 Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks Liang, Bin; Su, Hang; Gui, Lin. - : Elsevier, 2022 BASE Show details 2 Does semantics aid syntax? An empirical study on named entity recognition and classification Zhong, Xiaoshi; Cambria, Erik; Hussain, Amir. - : Springer, 2021 BASE Show details 3 Arabic question answering system: a survey Azmi, Aqil M.; Cambria, Erik; Hussain, Amir. - : Springer, 2021 BASE Show details 4 A novel context-aware multimodal framework for persian sentiment analysis Dashtipour, Kia; Gogate, Mandar; Cambria, Erik. - : Elsevier, 2021 BASE Show details 5 MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER ... Zhou, Ran; Li, Xin; He, Ruidan. - : arXiv, 2021 BASE Show details 6 Mood of the Planet: Challenging Visions of Big Data in the Arts Sorensen, Vibeke; Lansing, John Stephen; Thummanapalli, Nagaraju... In: Cognit Comput (2021) BASE Show details 7 A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis Dashtipour, Kia; Gogate, Mandar; Cambria, Erik. - : Elsevier BV, 2021 BASE Show details 8 Extracting Time Expressions and Named Entities with Constituent-Based Tagging Schemes Zhong, Xiaoshi; Cambria, Erik; Hussain, Amir. - : Springer, 2020 BASE Show details 9 personality-detection-using-bagged-svm-over-bert.pdf ... Amirmohammad Kazemeini; Fatehi, Samin; Yash Mehta. - : figshare, 2020 BASE Show details 10 personality-detection-using-bagged-svm-over-bert.pdf ... Amirmohammad Kazemeini; Fatehi, Samin; Yash Mehta. - : figshare, 2020 BASE Show details 11 Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks ... Ji, Shaoxiong; Li, Xue; Huang, Zi. - : arXiv, 2020 BASE Show details 12 Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles ... Kazameini, Amirmohammad; Fatehi, Samin; Mehta, Yash. - : arXiv, 2020 BASE Show details 13 Phonetic-enriched Text Representation for Chinese Sentiment Analysis with Reinforcement Learning ... Peng, Haiyun; Ma, Yukun; Poria, Soujanya. - : arXiv, 2019 BASE Show details 14 PhonSenticNet: A Cognitive Approach to Microtext Normalization for Concept-Level Sentiment Analysis ... Satapathy, Ranjan; Singh, Aalind; Cambria, Erik. - : arXiv, 2019 BASE Show details 15 Learning visual concepts in images using temporal convolutional networks Qian, Chen; Chaturvedi, Iti; Poria, Soujanya. - : Institute of Electrical and Electronics Engineers, 2019 BASE Show details 16 Inconsistencies on TripAdvisor reviews: a unified index between users and Sentiment Analysis Methods Valdivia, Ana; Hrabova, Emiliya; Chaturvedi, Iti. - : Elsevier, 2019 BASE Show details 17 Fuzzy commonsense reasoning for multimodal sentiment analysis Chaturvedi, Iti; Satapathy, Ranjan; Cavallari, Sandro. - : Elsevier, 2019 BASE Show details 18 Relation Extraction of Medical Concepts Using Categorization and Sentiment Analysis Mondal, Anupam; Cambria, Erik; Das, Dipankar. - : BMC, 2018 BASE Show details 19 Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis Ma, Yukun; Peng, Haiyun; Khan, Tahir; Cambria, Erik; Hussain, Amir. - : BMC, 2018 Abstract: Sentiment analysis has emerged as one of the most popular natural language processing (NLP) tasks in recent years. A classic setting of the task mainly involves classifying the overall sentiment polarity of the inputs. However, it is based on the assumption that the sentiment expressed in a sentence is unified and consistent, which does not hold in the reality. As a fine-grained alternative of the task, analyzing the sentiment towards a specific target and aspect has drawn much attention from the community for its more practical assumption that sentiment is dependent on a particular set of aspects and entities. Recently, deep neural models have achieved great successes on sentiment analysis. As a functional simulation of the behavior of human brains and one of the most successful deep neural models for sequential data, long short-term memory (LSTM) networks are excellent in learning implicit knowledge from data. However, it is impossible for LSTM to acquire explicit knowledge such as commonsense facts from the training data for accomplishing their specific tasks. On the other hand, emerging knowledge bases have brought a variety of knowledge resources to our attention, and it has been acknowledged that incorporating the background knowledge is an important add-on for many NLP tasks. In this paper, we propose a knowledge-rich solution to targeted aspect-based sentiment analysis with a specific focus on leveraging commonsense knowledge in the deep neural sequential model. To explicitly model the inference of the dependent sentiment, we augment the LSTM with a stacked attention mechanism consisting of attention models for the target level and sentence level, respectively. In order to explicitly integrate the explicit knowledge with implicit knowledge, we propose an extension of LSTM, termed Sentic LSTM. The extended LSTM cell includes a separate output gate that interpolates the token-level memory and the concept-level input. In addition, we propose an extension of Sentic LSTM by creating a hybrid of the LSTM and a recurrent additive network that simulates sentic patterns. In this paper, we are mainly concerned with a joint task combining the target-dependent aspect detection and targeted aspect-based polarity classification. The performance of proposed methods on this joint task is evaluated on two benchmark datasets. The experiment shows that the combination of proposed attention architecture and knowledge-embedded LSTM could outperform state-of-the-art methods in two targeted aspect sentiment tasks. We present a knowledge-rich solution for the task of targeted aspect-based sentiment analysis. Our model can effectively incorporate the commonsense knowledge into the deep neural network and be trained in an end-to-end manner. We show that the two-step attentive neural architecture as well as the proposed Sentic LSTM and H-Sentic-LSTM can achieve an improved performance on resolving the aspect categories and sentiment polarity for a targeted entity in its context over state-of-the-art systems. Keyword: Attention model; Commonsense knowledge; Neural network; Sentiment analysis URL: https://doi.org/10.1007/s12559-018-9549-x http://researchrepository.napier.ac.uk/Output/1792347 BASE Hide details 20 A Deep Learning Approach for Multimodal Deception Detection ... Krishnamurthy, Gangeshwar; Majumder, Navonil; Poria, Soujanya. - : arXiv, 2018 BASE Show details Page: 1 2 3

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