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funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts
2017
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
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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
2014
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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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
2018
Proceedings of The 12th International Workshop on Semantic Evaluation
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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
2018
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
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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
2019
Proceedings of the 2019 Conference of the North
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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
2018
Proceedings of The 12th International Workshop on Semantic Evaluation
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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
2018
Proceedings of The 12th International Workshop on Semantic Evaluation
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(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]
2020
arXiv
pre-print
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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
2019
International Journal of Advanced Computer Science and Applications
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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
2015
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)
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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
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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
2018
Proceedings of The 12th International Workshop on Semantic Evaluation
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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]
2020
arXiv
pre-print
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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]
2017
arXiv
pre-print
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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
2017
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
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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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