A Cognition Based Attention Model for Sentiment Analysis

Long, Yunfei; Lu, Qin; Xiang, Rong; Li, Minglei; Huang, Chu‐Ren

doi:10.18653/v1/d17-1048

Cited by 66 publications

(40 citation statements)

References 25 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In literature, many researchers used IMDB dataset with 2 classes for performance testing of proposed algorithms. Results of different techniques for text classification in IMDB dataset are available in [3,5,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. IMDB dataset for web opinion mining is investigated in [5].…”

Section: Related Workmentioning

confidence: 99%

Comparative Study ofConvolutional Neural Network with Word Embedding Technique for Text Classification

Adamuthe¹,

Jagtap²

2019

IJISA

View full text Add to dashboard Cite

This paper presents an investigation of the convolutional neural network (CNN) with Word2Vec word embedding technique for text classification. Performance of CNN is tested on seven benchmark datasets with a different number of classes, training and testing samples. Test classification results obtained from proposed CNN are compared with results of CNN models and other classifiers reported in the literature. Investigation shows that CNN models are better suitable for text classification than other techniques. The main objective of the paper is to identify best-fitted parameter values batch size, epochs, activation function, dropout rates and feature maps values. Results of proposed CNN are better than many other classification techniques reported in the literature for Yelp Review Polarity dataset and Amazon Review Polarity dataset. For all the seven datasets, accuracy obtained by proposed CNN is close to the best-known results from the literature.

show abstract

Section: Related Workmentioning

confidence: 99%

Comparative Study ofConvolutional Neural Network with Word Embedding Technique for Text Classification

Adamuthe¹,

Jagtap²

2019

IJISA

View full text Add to dashboard Cite

show abstract

“…Attention mechanism is also added to LSTM models to highlight important segments at both sentence level and document level. Attention models can be built from text in local context (Yang et al, 2016), user/production information Long et al, 2017a) and other information such as cognition grounded eye tracking data (Long et al, 2017b). LSTM models with attention mechanism are currently the state-of-theart models in document sentiment analysis tasks Long et al, 2017b).…”

Section: Neural Network Modelsmentioning

confidence: 99%

Dual Memory Network Model for Biased Product Review Classification

Long¹,

Ma²,

Lu³

et al. 2018

Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Self Cite

View full text Add to dashboard Cite

In sentiment analysis (SA) of product reviews, both user and product information are proven to be useful. Current tasks handle user profile and product information in a unified model which may not be able to learn salient features of users and products effectively. In this work, we propose a dual user and product memory network (DUPMN) model to learn user profiles and product reviews using separate memory networks. Then, the two representations are used jointly for sentiment prediction. The use of separate models aims to capture user profiles and product information more effectively. Compared to state-of-theart unified prediction models, the evaluations on three benchmark datasets, IMDB, Yelp13, and Yelp14, show that our dual learning model gives performance gain of 0.6%, 1.2%, and 0.9%, respectively. The improvements are also deemed very significant measured by p-values. arXiv:1809.05807v1 [cs.CL]

show abstract

“…Question Encoding A gate recurrent unit (GRU) [7] is used to encode the question embedding, which is widely adopted in NLP and multimodal tasks [17,22,41]. To be specific, given a question with T words Q = [q 1 , ..., q t , ..., q T ], where q t is the one hot vector of the question word at position t, we first embed them into a dense representation via a linear transformation x t = W e q t .…”

Section: Context-aware Visual Attentionmentioning

confidence: 99%

R-Vqa

Zhang

et al. 2018

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

View full text Add to dashboard Cite

Recently, Visual Question Answering (VQA) has emerged as one of the most significant tasks in multimodal learning as it requires understanding both visual and textual modalities. Existing methods mainly rely on extracting image and question features to learn their joint feature embedding via multimodal fusion or attention mechanism. Some recent studies utilize external VQA-independent models to detect candidate entities or attributes in images, which serve as semantic knowledge complementary to the VQA task. However, these candidate entities or attributes might be unrelated to the VQA task and have limited semantic capacities. To better utilize semantic knowledge in images, we propose a novel framework to learn visual relation facts for VQA. Specifically, we build up a Relation-VQA (R-VQA) dataset based on the Visual Genome dataset via a semantic similarity module, in which each data consists of an image, a corresponding question, a correct answer and a supporting relation fact. A well-defined relation detector is then adopted to predict visual question-related relation facts. We further propose a multi-step attention model composed of visual attention and semantic attention sequentially to extract related visual knowledge and semantic knowledge. We conduct comprehensive experiments on the two benchmark datasets, demonstrating that our model achieves state-of-the-art performance and verifying the benefit of considering visual relation facts.

show abstract

A Cognition Based Attention Model for Sentiment Analysis

Cited by 66 publications

References 25 publications

Comparative Study ofConvolutional Neural Network with Word Embedding Technique for Text Classification

Comparative Study ofConvolutional Neural Network with Word Embedding Technique for Text Classification

Dual Memory Network Model for Biased Product Review Classification

R-Vqa

Contact Info

Product

Resources

About