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Leveraging Multi-grained Sentiment Lexicon Information for Neural Sequence Models [article]

Yan Zeng, Yangyang Lan, Yazhou Hao, Chen Li, Qinhua Zheng
2019 arXiv   pre-print
Neural sequence models have achieved great success in sentence-level sentiment classification. However, some models are exceptionally complex or based on expensive features.  ...  Experimental results show that our method can increase classification accuracy for neural sequence models on both SST-5 and MR dataset.  ...  Related Work With the development of neural networks, many classical models based on neural networks have been applied into sentiment classification recently, which include Recursive Neural Network (  ... 
arXiv:1812.01527v2 fatcat:zbabyrrpc5fh5hmhi2jwzneraa

Lexicon-Enhanced Attention Network Based on Text Representation for Sentiment Classification

Wenkuan Li, Dongyuan Li, Hongxia Yin, Lindong Zhang, Zhenfang Zhu, Peiyu Liu
2019 Applied Sciences  
In this paper, we propose a lexicon-enhanced attention network (LAN) based on text representation to improve the performance of sentiment classification.  ...  Specifically, we first propose a lexicon-enhanced attention mechanism by combining the sentiment lexicon with an attention mechanism to incorporate sentiment linguistic knowledge into deep learning methods  ...  Hierarchical Lexicon-Enhanced Attention Network When faced with large-scale text, i.e., document-level sentiment classification, we design a hierarchical classification model based on LAN.  ... 
doi:10.3390/app9183717 fatcat:p2ta6h3dabd55p5e6fdfs7j33q

A Lexicon-Enhanced Attention Network for As-pect-Level Sentiment Analysis

Zhiying Ren, Guangping Zeng, Chen Liu, Qingchuan Zhang, Chunguang Zhang, Dingqi Pan
2020 IEEE Access  
Therefore, we propose a lexicon-enhanced attention network (LEAN) based on bidirectional LSTM.  ...  Moreover, leveraging lexicon information will enhance the model's flexibility and robustness.  ...  LEXICON ENHANCED SENTIMENT ANALYSIS In recent years, deep neural networks have been widely used in sentiment classification.  ... 
doi:10.1109/access.2020.2995211 fatcat:ucuxsyfxtvepfiufplr3tue7zq

An Improved Approach for Text Sentiment Classification Based on a Deep Neural Network via a Sentiment Attention Mechanism

Li, Liu, Zhang, Liu
2019 Future Internet  
In this paper, we propose a sentiment-feature-enhanced deep neural network (SDNN) to address the problem by integrating sentiment linguistic knowledge into a deep neural network via a sentiment attention  ...  Specifically, first we introduce a novel sentiment attention mechanism to help select the crucial sentiment-word-relevant context words by leveraging the sentiment lexicon in an attention mechanism, which  ...  To alleviate the aforementioned limitations, in this study, we propose a sentiment-feature-enhanced deep neural network (SDNN) for text sentiment classification.  ... 
doi:10.3390/fi11040096 fatcat:sqnrajx5hnhebfvulimmrt5orq

Sentiment Analysis for E-commerce Product Reviews in Chinese based on Sentiment Lexicon and Deep Learning

Li Yang, Ying Li, Jin Wang, R. Simon Sherratt
2020 IEEE Access  
The SLCABG model combines the advantages of the sentiment lexicon and deep learning techniques. First, the sentiment lexicon is used to enhance the sentiment features in the reviews.  ...  This paper proposes a new sentiment analysis model-SLCABG, which is based on the sentiment lexicon and combines Convolutional Neural Network (CNN) and attention-based Bidirectional Gated Recurrent Unit  ...  First, the sentiment lexicon is used to enhance the sentiment features in the reviews.  ... 
doi:10.1109/access.2020.2969854 fatcat:eh76xrdnwfhdfpl52lpto4ls3e

Sentiment Lexicon Construction with Representation Learning Based on Hierarchical Sentiment Supervision

Leyi Wang, Rui Xia
2017 Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing  
In this paper, we develop a neural architecture to train a sentiment-aware word embedding by integrating the sentiment supervision at both document and word levels, to enhance the quality of word embedding  ...  Experiments on the SemEval 2013-2016 datasets indicate that the sentiment lexicon generated by our approach achieves the state-of-the-art performance in both supervised and unsupervised sentiment classification  ...  Supervised Sentiment Classification Evaluation: To evaluate the effect of the sentiment lexicon in supervised sentiment classification, we report the supervised sentiment classification performance by  ... 
doi:10.18653/v1/d17-1052 dblp:conf/emnlp/WangX17 fatcat:ya2ctpidk5cvxmnibym3tsyrnu

Towards Twitter sentiment classification by multi-level sentiment-enriched word embeddings

Shufeng Xiong, Hailian Lv, Weiting Zhao, Donghong Ji
2018 Neurocomputing  
Most of existing work learn sentiment-specific word representation for improving Twitter sentiment classification, which encoded both n-gram and distant supervised tweet sentiment information in learning  ...  We develop a multi-level sentiment-enriched word embedding learning method, which uses parallel asymmetric neural network to model n-gram, word level sentiment and tweet level sentiment in learning process  ...  By using this corpus, they train a RNTN (Recursive Neural Tensor Network) model for sentiment classification.  ... 
doi:10.1016/j.neucom.2017.11.023 fatcat:vyhpvf5esfavdgmspsyvzuyiwe

A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification [article]

Zeyang Lei, Yujiu Yang, Min Yang, Yi Liu
2018 arXiv   pre-print
In this paper, we propose a Multi-sentiment-resource Enhanced Attention Network (MEAN) to alleviate the problem by integrating three kinds of sentiment linguistic knowledge (e.g., sentiment lexicon, negation  ...  Deep learning approaches for sentiment classification do not fully exploit sentiment linguistic knowledge.  ...  In this work, we propose a Multi-sentimentresource Enhanced Attention Network (MEAN) for sentence-level sentiment classification to integrate many kinds of sentiment linguistic knowledge into deep neural  ... 
arXiv:1807.04990v1 fatcat:ohyo4tjb2bg3jkwrpo7q6ur6fu

A multi-modal and multi-scale emotion-enhanced inference model based on fuzzy recognition

Yan Yu, Dong Qiu, Ruiteng Yan
2021 Complex & Intelligent Systems  
AbstractOnly the label corresponding to the maximum value of the fully connected layer is used as the output category when a neural network performs classification tasks.  ...  of the text based on the dictionary to establish a multi-modal fuzzy recognition emotion enhancement model.  ...  The overall architecture of sentiment analysis model fused by deep neural networks, emojis and lexicon-based sentiment enhancement fuzzy reasoning is listed in Fig. 1 .  ... 
doi:10.1007/s40747-021-00579-4 fatcat:xpqywqycjbddta4dvxk5f7kxpq

Predictive Analytics for Stock Prices using Sentiment Analysis

Salma Elsayed
2022 International Journal of Computer Applications  
These methods can be classified into four main categories, namely, machine learning, lexicon, graph, and hybrid based methods.  ...  ., open price and close price) or the sentiment analysis of social media text (e.g., tweets). In this paper, we will discuss the several approach of stock prediction using sentiment analysis methods.  ...  Sentiment Classification approaches can be classified into: machine learning, lexicon-based, and hybrid.  ... 
doi:10.5120/ijca2022921888 fatcat:vqep7lct4nepzbkfa3u7nglpam

A survey on sentiment analysis in tourism

sarah anis, Sally Saad, Mostafa Aref
2020 International Journal of Intelligent Computing and Information Sciences  
The main target of this survey is to give a nearly full image of sentiment analysis approaches, techniques, and challenges in analyzing the correct meaning of sentiments and detecting the suitable sentiment  ...  Sentiment analysis has great potential to directly understand tourists' opinions. This paper tackles a comprehensive overview of the latest update in this field.  ...  and thus enhancing the overall polarity sentiment classification.  ... 
doi:10.21608/ijicis.2020.106309 fatcat:hhmnterlezaeriuyywoghhnpi4

Adullam at SemEval-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention

Joosung Yoon, Kigon Lyu, Hyeoncheol Kim
2017 Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)  
The proposed method is based on lexicon integrated convolutional neural networks with attention (LCA). Its performance was evaluated using the datasets provided by SemEval competition (Task 4).  ...  We propose a sentiment analyzer for the prediction of document-level sentiments of English micro-blog messages from Twitter.  ...  Sentiment Lexicon (2013). • Bing Liu Opinion Lexicon (2004) .  ... 
doi:10.18653/v1/s17-2123 dblp:conf/semeval/YoonLK17 fatcat:moeaowexd5fltk5kivjke7ijia

Sentiment Analysis using Artificial Neural Network

2020 International journal of recent technology and engineering  
The paper presents a survey with main focus on performance of different artificial neural networks used for opinion mining or sentiment analysis while it also includes various machine learning approaches  ...  such as Naïve Bayes, Support Vector Machine, lexicon-based approach and Maximum Entropy.  ...  Then, Dynamic Artificial Neural Network was applied for the classification of the sentiment of the tweets where classification was again done in the two sets comprising three class and five class classification  ... 
doi:10.35940/ijrte.e6450.018520 fatcat:vyee4yzojje3bf7cm3zesm3k7i

Sentiment analysis on IMDB using lexicon and neural networks

Zeeshan Shaukat, Abdul Ahad Zulfiqar, Chuangbai Xiao, Muhammad Azeem, Tariq Mahmood
2020 SN Applied Sciences  
The area of analysis of sentiments is related closely to natural language processing and text mining.  ...  Sentiment analysis is an important field of study in machine learning that focuses on extracting information of subject from the textual reviews.  ...  For this study, we used an enhancement BoW mechanism as a baseline to evaluate and analyze sentimental reviews.  ... 
doi:10.1007/s42452-019-1926-x fatcat:bs45frynmzawhhiwvo2fcj5stq

A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification

Zeyang Lei, Yujiu Yang, Min Yang, Yi Liu
2018 Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)  
In this paper, we propose a Multi-sentiment-resource Enhanced Attention Network (MEAN) to alleviate the problem by integrating three kinds of sentiment linguistic knowledge (e.g., sentiment lexicon, negation  ...  Deep learning approaches for sentiment classification do not fully exploit sentiment linguistic knowledge.  ...  In this work, we propose a Multi-sentimentresource Enhanced Attention Network (MEAN) for sentence-level sentiment classification to integrate many kinds of sentiment linguistic knowledge into deep neural  ... 
doi:10.18653/v1/p18-2120 dblp:conf/acl/LeiYYL18 fatcat:ixj5vuydy5flhf4qqmhfh4f3ty
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