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Meta-Learner for Amharic Sentiment Classification

Girma Neshir, Andreas Rauber, Solomon Atnafu
2021 Applied Sciences  
These models in the framework are fused by a meta-learner (in this case, logistic regression) for Amharic sentiment classification.  ...  The overall results of the meta-learner (i.e., stack ensemble) have revealed performance rise over the base learners with TF-IDF character n-grams.  ...  The performance of ensemble learning for sentiment classification is reviewed.  ... 
doi:10.3390/app11188489 fatcat:uvqocezpmrdepmewigidhkdoka

The Today Tendency of Sentiment Classification [chapter]

Vo Ngoc Phu, Vo Thi Ngoc Tran
2018 Artificial Intelligence - Emerging Trends and Applications  
There are two approaches within the category of corpus-based approaches: The Today Tendency of Sentiment Classification  ...  There have been many kinds of the sentiment analysis such as machine learning approaches, lexicon-based approaches, etc., for many years.  ...  The authors of [11] propose a lexicon-based approach to sentiment classification of Twitter posts.  ... 
doi:10.5772/intechopen.74930 fatcat:w3wqlxtuzrba7gkqcj5og6w45a

Ensemble of feature sets and classification algorithms for sentiment classification

Rui Xia, Chengqing Zong, Shoushan Li
2011 Information Sciences  
In this paper, we make a comparative study of the effectiveness of ensemble technique for sentiment classification.  ...  The ensemble framework is applied to sentiment classification tasks, with the aim of efficiently integrating different feature sets and classification algorithms to synthesize a more accurate classification  ...  The weights assigned to each component are learned automatically via machine learning techniques and represent the relevance of corresponding components to sentiment classification.  ... 
doi:10.1016/j.ins.2010.11.023 fatcat:z4mxmo4hufggdhkzwgn7fu46ue

Analysis of Various Sentiment Classification Techniques

Vimalkumar B., Bhumika M.
2016 International Journal of Computer Applications  
Sentiment analysis is an ongoing research area in the field of text mining.  ...  The main contribution of this paper is to give idea about that careful feature selection and existing classification approaches can give better accuracy.  Convert upper to lower case letter, remove Punctuation  ...  Sentiment classification in machine learning consists of two steps.  ... 
doi:10.5120/ijca2016909259 fatcat:r32jcbc26zg5lc7qj6mzenmk4e

Learning Bilingual Sentiment Word Embeddings for Cross-language Sentiment Classification

HuiWei Zhou, Long Chen, Fulin Shi, Degen Huang
2015 Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)  
The proposed B-SWE incorporate sentiment information of text into bilingual embeddings.  ...  Bilingual embeddings could eliminate the semantic gap between two languages for CLSC, but ignore the sentiment information of text.  ...  Acknowledgments We wish to thank the anonymous reviewers for their valuable comments. This research is supported by National Natural Science Foundation of China (Grant No. 61272375).  ... 
doi:10.3115/v1/p15-1042 dblp:conf/acl/ZhouCSH15 fatcat:vwpifqisr5h6hl35ycrzdqgyzy

Sentiment Classification Using Convolutional Neural Networks

Kim, Jeong
2019 Applied Sciences  
Recently, the ConvolutionalNeural Network (CNN) has been adopted for the task of text classification and has shown quitesuccessful results.  ...  The texts may contain various labels suchas gender, age, country, sentiment, and so forth.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app9112347 fatcat:mzlu5ohftrcftmyec42xudblra

Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification [article]

Xiaochen Hou, Jing Huang, Guangtao Wang, Xiaodong He, Bowen Zhou
2021 arXiv   pre-print
Aspect-level sentiment classification aims to identify the sentiment polarity towards a specific aspect term in a sentence.  ...  In this paper, we propose to employ graph convolutional networks (GCNs) on the dependency tree to learn syntax-aware representations of aspect terms.  ...  Conclusions We propose a selective attention based GCN model for the aspect-level sentiment classification task.  ... 
arXiv:1910.10857v4 fatcat:5w2nqjodfzdrxjojndqkzzno74

Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus

Danushka Bollegala, David Weir, John Carroll
2013 IEEE Transactions on Knowledge and Data Engineering  
Typically, sentiment classification has been modeled as the problem of training a binary classifier using reviews annotated for positive or negative sentiment.  ...  Abstract-Automatic classification of sentiment is important for numerous applications such as opinion mining, opinion summarization, contextual advertising, and market analysis.  ...  of features and construct a sentiment sensitive thesaurus.  ... 
doi:10.1109/tkde.2012.103 fatcat:ho4d4d5gengqxni5njok3yjxqy

Domain Independent Sentiment Classification with Many Lexicons

Bruno Ohana, Brendan Tierney, Sarah-Jane Delany
2011 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications  
We present a comparative study of sentiment classification of reviews on six different domains using sentiment lexicons from different sources.  ...  Sentiment lexicons are language resources widely used in opinion mining and important tools in unsupervised sentiment classification.  ...  ACKNOWLEDGMENT We wish to thank Professor Bing Liu from University of Chicago Illinois and his team for providing access to a large set of user generated review data, enabling the creation of some of the  ... 
doi:10.1109/waina.2011.103 dblp:conf/aina/OhanaTD11 fatcat:6aphcjwgqnd4bjgal5nq4rvqvy

Automated Classification of Text Sentiment [article]

Emmanuel Dufourq, Bruce A. Bassett
2018 arXiv   pre-print
This increases, decreases or negates the sentiment of the following word. The sentiment of the full text is then the sum of these terms.  ...  The GAs learn whether words occurring in a text corpus are either sentiment or amplifier words, and their corresponding magnitude.  ...  The computations were performed at the University of Geneva on the Baobab cluster.  ... 
arXiv:1804.01963v1 fatcat:3xjr64gpbnbwbdsrpmgqj6b4im

Sentiment Classification using Subjective and Objective Views

Suke Li
2013 International Journal of Computer Applications  
This work is trying to combine two kinds of views to carry out sentiment classification.  ...  This work proposes a new semi-supervised sentiment classification method by exploiting a large number of unlabeled instances to conduct sentiment classification for Web consumer reviews.  ...  [8] made a comparative study of the effectiveness of ensemble technique for sentiment classification.  ... 
doi:10.5120/13875-1749 fatcat:rttadia5w5glvharzu6ocoh3v4


Abbas Jalilvand, Naomie Salim
2016 Jurnal Teknologi  
In this paper, a stream sentiment classification framework is proposed to deal with concept drift and imbalanced data distribution using ensemble learning and instance selection methods.  ...  The experimental results show the effectiveness of the proposed method in compared with static sentiment classification.  ...  In summary, the main contribution of this paper is a new methodology for stream sentiment classification, which can track changing user's opinions.  ... 
doi:10.11113/jt.v78.10120 fatcat:mxyedcbnrzhmpcp7o2mosplaba

Cross-Lingual Mixture Model for Sentiment Classification

Xinfan Meng, Furu Wei, Xiaohua Liu, Ming Zhou, Ge Xu, Houfeng Wang
2012 Annual Meeting of the Association for Computational Linguistics  
By fitting parameters to maximize the likelihood of the bilingual parallel data, the proposed model learns previously unseen sentiment words from the large bilingual parallel data and improves vocabulary  ...  Such a disproportion arouse interest in cross-lingual sentiment classification, which aims to conduct sentiment classification in the target language (e.g.  ...  s and λ t ) controlling the contribution of unlabeled parallel data.  ... 
dblp:conf/acl/MengWLZXW12 fatcat:pdb3natzfvhv5mcgnpfy5m5734

Intelligent Hybrid Feature Selection for Textual Sentiment Classification

Jawad Khan, Aftab Alam, Youngmoon Lee
2021 IEEE Access  
Finally, for textual sentiment classification, the well-known classification algorithms Support Vector Machine (SVM), Naive Bayes (NB), Generalized Linear Model (GLM) are trained in the ensemble model  ...  The sentiment features subset is then selected employing a fast and simple rank-based ensemble of multiple filters feature selection method.  ...  CLASSIFICATION ALGORITHMS AND ENSEMBLE LEARNING METHOD In this section, we briefly discuss the classification algorithms with ensemble learning method for sentiment classification.  ... 
doi:10.1109/access.2021.3118982 fatcat:etece4olsrdjdojpewvpt53jbu

Multi-task Learning for Target-dependent Sentiment Classification [article]

Divam Gupta, Kushagra Singh, Soumen Chakrabarti, Tanmoy Chakraborty
2019 arXiv   pre-print
In this paper, we present MTTDSC, a multi-task target-dependent sentiment classification system that is informed by feature representation learnt for the related auxiliary task of passage-level sentiment  ...  In the main task, these GRUs contribute auxiliary per-token representations over and above word embeddings. The main task has its own, separate GRUs.  ...  Possibly because research on passage-level and target-dependent sentiment classification were separated in time by the dramatic emergence of deep learning, TDSC systems predominantly use recurrent neural  ... 
arXiv:1902.02930v1 fatcat:r6wpk22vf5af3czihknerbraiu
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