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Refined Global Word Embeddings Based on Sentiment Concept for Sentiment Analysis

Yabing Wang, Guimin Huang, Jun Li, Hui Li, Ya Zhou, Hua Jiang
2021 IEEE Access  
of sentiment information and provide more accurate semantics and sentiment representation for words.  ...  Then we obtained the sentiment information of words under optimal sentiment concept from the multi-semantics sentiment intensity lexicon which we constructed in this paper to achieve accurate embedding  ...  In this paper, we provide more accurate semantics and sentiment representation for words by the proposed method. III.  ... 
doi:10.1109/access.2021.3062654 fatcat:d3i2dnrnjfeovks5raig5wq6xi

Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings

Raksha Sharma, Arpan Somani, Lakshya Kumar, Pushpak Bhattacharyya
2017 Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing  
We show that sentiment bearing word embeddings facilitate a more accurate intensity ranking system than other standard word embeddings (word2vec and GloVe).  ...  Identification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a finegrained sentiment analysis.  ...  Acknowledgments We heartily thank English linguists Rajita Shukla and Jaya Saraswati from CFILT Lab, IIT Bombay for giving their valuable contribution in the gold standard data creation.  ... 
doi:10.18653/v1/d17-1058 dblp:conf/emnlp/SharmaSKB17 fatcat:orshw4exz5hnfauptjq2hlb3o4

Sentiment Analysis using Multiple Word Embedding for Words

2020 International journal of recent technology and engineering  
The proposed model combines these two embeddings to generate true semantics and task specific word embeddings. Result analysis shows that the proposed system works better on many benchmark dataset  ...  For these networks, words are represented by using vectors called word embeddings. The required word embeddings are taken from pre-trained Word2Vec or learned from a corpus of the given main task.  ...  Twitter Dataset [20] : This dataset consists of more than 1.5 million twits extracted from Twitter for sentiment analysis.  ... 
doi:10.35940/ijrte.a2606.059120 fatcat:ckof3n2txferbjxrxysy3vom2e

Robust Visual-Textual Sentiment Analysis

Quanzeng You, Liangliang Cao, Hailin Jin, Jiebo Luo
2016 Proceedings of the 2016 ACM on Multimedia Conference - MM '16  
Our system first builds a semantic tree structure based on sentence parsing, aimed at aligning textual words and image regions for accurate analysis.  ...  Sentiment analysis is crucial for extracting social signals from social media content.  ...  Acknowledgment This work was generously supported in part by Adobe Research and New York State through the Goergen Institute for Data Science at the University of Rochester.  ... 
doi:10.1145/2964284.2964288 dblp:conf/mm/YouCJL16 fatcat:jwftcdellngpblor5mpbhooq6i

Sentiment Enhanced Multi-modal Hashtag Recommendation for Micro-Videos

Chao Yang, Xiaochan Wang, Bin Jiang
2020 IEEE Access  
Furthermore, the varying importance of the multimodal sentiment and content features are dynamically captured via an attention neural network according to their consistency with the hashtag semantic embedding  ...  Specifically, the multi-modal content features and the multi-modal sentiment features are modeled by a content common space learning branch based on self-attention and a sentiment common space learning  ...  to obtain more accurate sentiment information for micro-videos; 2)We will strive to explore more effective interactions between micro-video sentiment information, content information, and hashtag semantic  ... 
doi:10.1109/access.2020.2989473 fatcat:n4uxa34lj5hdzdbqflv5sjdqpm

UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages [article]

Ehsaneddin Asgari and Fabienne Braune and Benjamin Roth and Christoph Ringlstetter and Mohammad R.K. Mofrad
2019 arXiv   pre-print
Sentiment lexica are vital for sentiment analysis in absence of document-level annotations, a very common scenario for low-resource languages.  ...  In this work, we use a massively parallel Bible corpus to project sentiment information from English to other languages for sentiment analysis on Twitter data.  ...  Using the language model based embedding space as the representation has two major benefits: First, the semantic continuity of the embedding space allows for propagation of sentiment labels to a larger  ... 
arXiv:1904.09678v2 fatcat:rvnorvxmqvdifaoopwczj7rvi4

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  
However, these existing models focus on more semantic information rather than sentiment linguistic knowledge, which provides rich sentiment information and plays a key role in sentiment analysis.  ...  Recently, deep learning-based representation models have achieved great success for sentiment classification.  ...  Acknowledgments: The authors are grateful to the supports from the Shandong Normal University for the server used for experiments. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app9183717 fatcat:p2ta6h3dabd55p5e6fdfs7j33q

SAKG-BERT: Enabling Language Representation With Knowledge Graphs for Chinese Sentiment Analysis

Xiaoyan Yan, Fanghong Jian, Bo Sun
2021 IEEE Access  
We propose a sentiment analysis knowledge graph (SAKG)-BERT model that combines sentiment analysis knowledge and the language representation model BERT.  ...  Our investigation reveals promising results in sentence completion and sentiment analysis tasks. INDEX TERMS Sentiment analysis, pretraining model, knowledge graph, deep learning, car reviews.  ...  Jimmy Huang for his advice and encouragement during the preparation of this article.  ... 
doi:10.1109/access.2021.3098180 fatcat:hof5rwm7w5eopkehpawvnxcflu

Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training [article]

Peng Xu, Andrea Madotto, Chien-Sheng Wu, Ji Ho Park, Pascale Fung
2018 arXiv   pre-print
Our evaluation of Emo2Vec shows that it outperforms existing affect-related representations, such as Sentiment-Specific Word Embedding and DeepMoji embeddings with much smaller training corpora.  ...  We train Emo2Vec by multi-task learning six different emotion-related tasks, including emotion/sentiment analysis, sarcasm classification, stress detection, abusive language classification, insult detection  ...  Related work For sentiment analysis, numerous classification models (Kalchbrenner et al.; Iyyer et al., 2015; Dou, 2017) have been explored.  ... 
arXiv:1809.04505v1 fatcat:qui4xanya5estaqmqaqyx7jsgm

Delta Embedding Learning [article]

Xiao Zhang, Ji Wu, Dejing Dou
2019 arXiv   pre-print
Evaluation also confirms the tuned word embeddings have better semantic properties.  ...  However there are limitations to the semantics represented by unsupervised embeddings, and inadequate fine-tuning of embeddings can lead to suboptimal performance.  ...  For example, words with a strong sentiment or polarity like "bore" and "fun" is mostly learned in sentiment analysis tasks.  ... 
arXiv:1812.04160v2 fatcat:bsldyigrrjhbjl76jz67im4n3y

Skin Tone Emoji and Sentiment on Twitter

Steven Coats
2018 arXiv   pre-print
It can be shown that values for the skin tone emoji by country correspond approximately to the skin tone of the resident populations, and that a negative correlation exists between tweet sentiment and  ...  Acknowledgement The author thanks Finland's Centre for Scientific Computing (CSC) for providing access to computational and data storage facilities.  ...  Word Embeddings in Multidimensional Space Recent work in many types of Natural Language Processing has seen widespread use of word embeddings for tasks ranging from translation to content extraction, part-ofspeech  ... 
arXiv:1805.00444v1 fatcat:b6tvl2oivfdkri4c2fyn5ih76e

A Survey of Sentiment Analysis Based on Transfer Learning

Ruijun LIU, Yuqian SHI, Changjiang JI, Ming JIA
2019 IEEE Access  
opportunities and challenges to the sentiment analysis.  ...  forward to the development trend of the sentiment analysis.  ...  [49] proposed EMLo (Embeddings from Language Models), which is a method for extracting word vectors of deep semantic features.  ... 
doi:10.1109/access.2019.2925059 fatcat:m7fpkulrcrcsfno523uzjxai4q

emoji2vec: Learning Emoji Representations from their Description [article]

Ben Eisner, Tim Rocktäschel, Isabelle Augenstein, Matko Bošnjak, Sebastian Riedel
2016 arXiv   pre-print
We demonstrate, for the downstream task of sentiment analysis, that emoji embeddings learned from short descriptions outperforms a skip-gram model trained on a large collection of tweets, while avoiding  ...  Many current natural language processing applications for social media rely on representation learning and utilize pre-trained word embeddings.  ...  Acknowledgements The authors would like to thank Michael Large, Peter Gabriel and Suran Goonatilake for the inspiration of this work, and the anonymous reviewers for their insightful comments.  ... 
arXiv:1609.08359v2 fatcat:c6zxebq7ofg4fnkf363tfwnarm

BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning in Sentiment Analysis [article]

Ahmed Murtadha, Shengfeng Pan, Bo Wen, Jianlin Su, Wenze Zhang, Yunfeng Liu
2022 arXiv   pre-print
Aspect-based sentiment analysis (ABSA) task aims to associate a piece of text with a set of aspects and meanwhile infer their respective sentimental polarities.  ...  Specifically, we first introduce a simple but effective mechanism that collaborates the semantic and syntactic information to construct auxiliary-sentences for the implicit aspect.  ...  Inspired by this intuition, we exploit the semantic distribution of the seed in the embedding space to capture more coherent indicators.  ... 
arXiv:2203.11702v1 fatcat:re2ibha7crfzxi4ospnfb7is6a

Multilingual Visual Sentiment Concept Matching

Nikolaos Pappas, Miriam Redi, Mercan Topkara, Brendan Jou, Hongyi Liu, Tao Chen, Shih-Fu Chang
2016 Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval - ICMR '16  
We then use word embeddings to repre- sent these concepts in a low dimensional vector space, allowing us to expand the meaning around concepts, and thus enabling insight about commonalities and differences  ...  semantic relatedness.  ...  Clustering Analysis. Which languages are more similar when tagging portraits?  ... 
doi:10.1145/2911996.2912016 dblp:conf/mir/PappasRTJLCC16 fatcat:l3jgpy2nzbg2fdfi3ei7oj4kpa
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