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funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts

Quanzhi Li, Armineh Nourbakhsh, Xiaomo Liu, Rui Fang, Sameena Shah
2017 Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)  
We use two types of word embeddings in our classifiers: the general word embeddings learned from 200 million tweets, and sentiment-specific word embeddings learned from 10 million tweets using distance  ...  We also incorporate a text feature model in our algorithm. This model produces features based on text negation, tf.idf weighting scheme, and a Rocchio text classification method.  ...  Our approach uses word embeddings (WE) learned from general tweets, sentiment specific word embeddings (SSWE) learned from distance supervised tweets, and a weighted text feature model (WTM).  ... 
doi:10.18653/v1/s17-2125 dblp:conf/semeval/LiNLFS17 fatcat:sdwnjqfy7vcgdffkyoetfm3kpu

Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification

Duyu Tang, Furu Wei, Nan Yang, Ming Zhou, Ting Liu, Bing Qin
2014 Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
To obtain large scale training corpora, we learn the sentiment-specific word embedding from massive distant-supervised tweets collected by positive and negative emoticons.  ...  Specifically, we develop three neural networks to effectively incorporate the supervision from sentiment polarity of text (e.g. sentences or tweets) in their loss functions.  ...  Acknowledgments We thank Yajuan Duan, Shujie Liu, Zhenghua Li, Li Dong, Hong Sun and Lanjun Zhou for their great help.  ... 
doi:10.3115/v1/p14-1146 dblp:conf/acl/TangWYZLQ14 fatcat:bryqvorbvzaubokgalw2ssnglm

THU_NGN at SemEval-2018 Task 1: Fine-grained Tweet Sentiment Intensity Analysis with Attention CNN-LSTM

Chuhan Wu, Fangzhao Wu, Junxin Liu, Zhigang Yuan, Sixing Wu, Yongfeng Huang
2018 Proceedings of The 12th International Workshop on Semantic Evaluation  
Traditional sentiment analysis approaches mainly focus on classifying the sentiment polarities or emotion categories of texts. However, they can't exploit the sentiment intensity information.  ...  A CNN layer with different kernel sizes is used to extract local features. The dense layers take the pooled CNN feature maps and predict the intensity scores.  ...  This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0800402 and in part by the National Natural Science Foundation of China under Grant U1705261  ... 
doi:10.18653/v1/s18-1028 dblp:conf/semeval/WuWLYWH18 fatcat:nkcingorrratpegxispjmbb5rq

Language Independent Sentiment Analysis with Sentiment-Specific Word Embeddings

Carl Saroufim, Akram Almatarky, Mohammad Abdel Hady
2018 Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis  
Word embeddings such as Word2Vec are efficient at incorporating semantic and syntactic properties of words, yielding good results for document classification.  ...  We train Sentiment Specific Word Embeddings (SSWE) on top of an unsupervised Word2Vec model, using either Recurrent Neural Networks (RNN) or Convolutional Neural Networks (CNN) on data auto-labeled as  ...  Through backpropagation, the weights of the Embedding Layer will be adjusted and incorporate sentiment.  ... 
doi:10.18653/v1/w18-6204 dblp:conf/wassa/SaroufimAA18 fatcat:snygald7yzableupt5cgetqnxu

Incorporating Emoji Descriptions Improves Tweet Classification

Abhishek Singh, Eduardo Blanco, Wei Jin
2019 Proceedings of the 2019 Conference of the North  
Specifically, we obtain new state-of-the-art results in irony detection and sentiment analysis despite our neural network is simpler than previous proposals.  ...  In this paper, we present a simple strategy to process emojis: replace them with their natural language description and use pretrained word embeddings as normally done with standard words.  ...  Acknowledgments This material is based upon work supported by the National Science Foundation under Grants Nos. 1734730, 1832267 and 1845757.  ... 
doi:10.18653/v1/n19-1214 dblp:conf/naacl/SinghBJ19 fatcat:cthzdzf37ja7plq3pswxofdf4m

THU_NGN at SemEval-2018 Task 2: Residual CNN-LSTM Network with Attention for English Emoji Prediction

Chuhan Wu, Fangzhao Wu, Sixing Wu, Zhigang Yuan, Junxin Liu, Yongfeng Huang
2018 Proceedings of The 12th International Workshop on Semantic Evaluation  
We also incorporated additional features such as POS tags and sentiment features extracted from lexicons.  ...  Thus, in SemEval-2018 Task 2 an interesting and challenging task is proposed, i.e., predicting which emojis are evoked by text-based tweets.  ...  Acknowledgments The authors thank the reviewers for their insightful comments and constructive suggestions on improving this work. This work was sup-  ... 
doi:10.18653/v1/s18-1063 dblp:conf/semeval/WuWWYLH18 fatcat:ttllgqelozb4vbbga5ousddjwe

DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning

Habibeh Naderi, Behrouz Haji Soleimani, Saif Mohammad, Svetlana Kiritchenko, Stan Matwin
2018 Proceedings of The 12th International Workshop on Semantic Evaluation  
(5) features derived from various sentiment and emotion lexicons, and (6) other hand-crafted features.  ...  tweets that contain emotion words (a distant supervision corpus), (3) word embeddings trained on the distant supervision corpus and averaged over all words in a tweet, (4) word and character n-grams,  ...  Acknowledgments We thank Xiang Jiang for helping us build attentive deep neural networks and fruitful discussions.  ... 
doi:10.18653/v1/s18-1045 dblp:conf/semeval/NaderiSMKM18 fatcat:viwrbv7bn5gcti5fp2hu4bmta4

Exploiting Class Labels to Boost Performance on Embedding-based Text Classification [article]

Arkaitz Zubiaga
2020 arXiv   pre-print
Embeddings of different kinds have recently become the de facto standard as features used for text classification.  ...  To make the most of these embeddings as features and to boost the performance of classifiers using them, we introduce a weighting scheme, Term Frequency-Category Ratio (TF-CR), which can weight high-frequency  ...  produce a dataset of tweets annotated for sentiment analysis by using distant supervision following [9] , leading to tweets annotated as positive or negative.  ... 
arXiv:2006.02104v2 fatcat:tjl2ki6vvnat5miozcmyrhowcq

Establishing News Credibility using Sentiment Analysis on Twitter

Zareen Sharf, Zakia Jalil, Wajiha Amir, Nudrat Siddiqui
2019 International Journal of Advanced Computer Science and Applications  
Sentiment Analysis or Opinion Mining is the system that intelligently performs classification of sentiments by extracting those opinions or sentiments from the given text (or comments or reviews).  ...  This paper presents a thorough research work carried out on tweets' sentiment analysis.  ...  ., 2013) [5] worked on the role of text pre-processing for sentiment analysis and demonstrated how the sentiment analysis can be further significantly improved by using appropriate feature selections  ... 
doi:10.14569/ijacsa.2019.0100927 fatcat:a72n2hxzbvgozerfx5ixz3as7i

WarwickDCS: From Phrase-Based to Target-Specific Sentiment Recognition

Richard Townsend, Adam Tsakalidis, Yiwei Zhou, Bo Wang, Maria Liakata, Arkaitz Zubiaga, Alexandra Cristea, Rob Procter
2015 Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)  
We present and evaluate several hybrid systems for sentiment identification for Twitter, both at the phrase and document (tweet) level.  ...  We also analyse techniques based on syntactic parsing and tokenbased association to handle topic specific sentiment in subtask C.  ...  Acknowledgements This work was partially funded by the Engineering and Physical Sciences Research Council (grant EP/ L016400/1) through the University of Warwick's Centre for Doctoral Training in Urban  ... 
doi:10.18653/v1/s15-2110 dblp:conf/semeval/TownsendTZWLZCP15 fatcat:ccydkh4elbdptnhi4ghk6wdx7i

A Novel Integrated Framework for Sarcasm Detection In Social Platform

2020 International Journal of Engineering and Advanced Technology  
Sarcasm identification in social media is a crucial facet of the sentiment analysis process, since it deals with texts whose polarity is completely opposite from its utterance.  ...  This paper also introduces a novel integrated framework for identifying sarcastic clues in tweets, and recognizing sarcastic users.  ...  The word embedding similarity is calculated using 2 methodsunweighted similarity features (UWS) and weighted similarity features (WS).  ... 
doi:10.35940/ijeat.d7519.049420 fatcat:cnstgqhnkbhmndhcenmbu5jp24

THU_NGN at SemEval-2018 Task 3: Tweet Irony Detection with Densely connected LSTM and Multi-task Learning

Chuhan Wu, Fangzhao Wu, Sixing Wu, Junxin Liu, Zhigang Yuan, Yongfeng Huang
2018 Proceedings of The 12th International Workshop on Semantic Evaluation  
In addition, we incorporate several types of features to improve the model performance. Our model achieved an F-score of 70.54 (ranked 2/43) in the subtask A and 49.47 (ranked 3/29) in the subtask B.  ...  Therefore, the Semeval-2018 task 3 is aimed to detect the ironic tweets (subtask A) and their irony types (subtask B).  ...  This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0800402 and in part by the National Natural Science Foundation of China under Grant U1705261  ... 
doi:10.18653/v1/s18-1006 dblp:conf/semeval/WuWWLYH18 fatcat:ct4o7zvxvbgcbkukw4rc47knpa

Effect of Text Processing Steps on Twitter Sentiment Classification using Word Embedding [article]

Manar D. Samad, Nalin D. Khounviengxay, Megan A. Witherow
2020 arXiv   pre-print
Processing of raw text is the crucial first step in text classification and sentiment analysis.  ...  The proposed tweet embedding is robust to and alleviates the need for several text processing steps.  ...  Expanding acronym Positive effects in general the text domain (Tweets) and the contextual (sentiment analysis) representation of words in the feature vector obtained via word embedding.  ... 
arXiv:2007.13027v1 fatcat:zmeqnhalxfgx7aocjcehkvmkdi

FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings [article]

Pedro Saleiro, Eduarda Mendes Rodrigues, Carlos Soares, Eugénio Oliveira
2017 arXiv   pre-print
We modeled the task as a regression analysis problem and combined traditional techniques such as pre-processing short texts, bag-of-words representations and lexical-based features with enhanced financial  ...  We used an external collection of tweets and news headlines mentioning companies/stocks from S\&P 500 to create financial word embeddings which are able to capture domain-specific syntactic and semantic  ...  We start by describing SemEval-2017 Task 5 and how we created financial-specific word embeddings.  ... 
arXiv:1704.05091v1 fatcat:uzllmg3upbd47kzrjvnvl2v4ee

FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings

Pedro Saleiro, Eduarda Mendes Rodrigues, Carlos Soares, Eugénio Oliveira
2017 Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)  
We used an external collection of tweets and news headlines mentioning companies/stocks from S&P 500 to create financial word embeddings which are able to capture domain-specific syntactic and semantic  ...  We modeled the task as a regression analysis problem and combined traditional techniques such as pre-processing short texts, bag-of-words representations and lexical-based features with enhanced financial  ...  We start by describing SemEval-2017 Task 5 and how we created financial-specific word embeddings.  ... 
doi:10.18653/v1/s17-2155 dblp:conf/semeval/SaleiroRSO17 fatcat:pcuioshqd5bnzl62ij2xqgf5jm
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