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Sentiment Mining Model for Opinionated Afaan Oromo Texts

Tariku Birhanu Yadesa, Syed Umar, Tagay Takele Fikadu
2020 International Journal of Scientific Research in Science Engineering and Technology  
The performance the system can be increased if stemming algorithm is improved, standard test corpus is used, and thesaurus is used to handle polysemy and synonymy words in the language.  ...  and stemming are used for this system(sentiment mining model for opinionated afaan Oromo texts).  ...  Sentence level: In the sentiment words detection component, all possible sentiment words and contextual valence shifter terms (negation terms and intensifier terms) are checked for existence in the sentiment  ... 
doi:10.32628/ijsrset2073131 fatcat:qhly7rxzwvbnrer724svenwuk4

Data-Driven Contextual Valence Shifter Quantification for Multi-Theme Sentiment Analysis

Hongkun Yu, Jingbo Shang, Meichun Hsu, Malu Castellanos, Jiawei Han
2016 Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM '16  
Moreover, contextual valence shifters may change sentiment polarity depending on the contexts that they appear in.  ...  themes, and (2) discovery and quantification of contextual valence shifters.  ...  and quantify contextual valence shifters based on training data.  ... 
doi:10.1145/2983323.2983793 pmid:28232874 pmcid:PMC5319421 dblp:conf/cikm/YuSHCH16 fatcat:ybvtziovljeatotiee5435pt6e

SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS

Alistair Kennedy, Diana Inkpen
2006 Computational intelligence  
We show that extending the term-counting method with contextual valence shifters improves the accuracy of the classification.  ...  The accuracy of classification is very high, and the valence shifter bigrams slightly improve it.  ...  at NRC/IIT for giving us access to their local copy of this system.  ... 
doi:10.1111/j.1467-8640.2006.00277.x fatcat:wuebbv6dofdmjimsmv4g34p4wq

Sentiment Classification of Drug Reviews Using a Rule-Based Linguistic Approach [chapter]

Jin-Cheon Na, Wai Yan Min Kyaing, Christopher S. G. Khoo, Schubert Foo, Yun-Ke Chang, Yin-Leng Theng
2012 Lecture Notes in Computer Science  
MetaMap, a medical resource tool, is used to identify various disease terms in the review documents to utilize domain knowledge for sentiment classification.  ...  Clause-level sentiment classification algorithm is developed and applied to drug reviews on a discussion forum.  ...  In contrast to most studies which focused on document-level or sentence-level sentiment analysis, our approach uses clause-level sentiment analysis so that different opinions on multiple aspects expressed  ... 
doi:10.1007/978-3-642-34752-8_25 fatcat:h42eb6sagfb5nbo6wuzpdqtvqe

Polarity Shift Handling Techniques: A Survey

Daya Mary, Hema Krishnan
2017 International Journal of Computer Applications  
The opinions mostly expressed in social networking sites can be harnessed through automated methods using sentiment analysis.  ...  Polarity classification is the most classical sentiment analysis task which aims at classifying reviews into either positive, negative or neutral.  ...  They show that extending the term-counting method with contextual valence shifters improves the accuracy of the classification. The second method uses a machine learning algorithm like SVM.  ... 
doi:10.5120/ijca2017912802 fatcat:uv2ekngaure35asilevlr6npwm

Study of Automatic Extraction, Classification, and Ranking of Product Aspects Based on Sentiment Analysis of Reviews

Muhammad Rafi, Muhammad Rafay, Usama Noman, Abdul Rehman, Umair Ali
2015 International Journal of Advanced Computer Science and Applications  
Moreover, the paper also explained the effect of Negation, Valence Shifter, and Diminisher with sentiment lexiconon sentiment analysis, andconcluded that they all are independent of the case problem, and  ...  have no effect on the accuracy of sentiment analysis.  ...  They specifically examine three types of contextual valence shifters namely negations, intensifier, and diminisher, and studies their effect on classification of reviews.  ... 
doi:10.14569/ijacsa.2015.061034 fatcat:e5suubyiabd5vfoxesxfhn2t7a

Data intensive review mining for sentiment classification across heterogeneous domains

Federica Bisio, Paolo Gastaldo, Chiara Peretti, Rodolfo Zunino, Erik Cambria
2013 Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM '13  
The paper tackles two crucial aspects of the sentiment classification problem: first, the computational complexity of the deployed framework; second, the ability of the framework itself to operate effectively  ...  To attain an objective measurement of the actual accuracy of the sentiment classification method, a campaign of tests involved a pair of complex, real-world scoring domains; the goal was to compare the  ...  The modulation strategy considers the presence of contextual valence shifters and the emotional valence of each term.  ... 
doi:10.1145/2492517.2500280 dblp:conf/asunam/BisioGPZC13 fatcat:qbcm2djmkva2bhg3j4hhslzwwm

Capturing Contextual Factors in Sentiment Classification: An Ensemble Approach

Thien Khai Tran, Tuoi Thi Phan
2020 IEEE Access  
INDEX TERMS Sentiment classification, contextual valence shifters, ensemble learning, deep learning, attention mechanism.  ...  Sentiment classification is a crucial task in sentiment analysis, and has received significant attention from researchers.  ...  These approaches remain ineffective in dealing with some linguistic phenomena, especially with a contextual valence shifter.  ... 
doi:10.1109/access.2020.3004180 fatcat:ro74lbephjgkfj3llir3lqz7y4

Contextual sentiment analysis for social media genres

Aminu Muhammad, Nirmalie Wiratunga, Robert Lothian
2016 Knowledge-Based Systems  
Lexical Valence Shifters Lexical valence shifters are typically used to increase sentiment (i.e. intensifiers, e.g., 'very', 'highly' ); decrease sentiment (i.e. diminishers, e.g., 'slightly', 'somewhat  ...  It takes as input the document to be classified, the hybrid lexicon, and the lists of lexical valence shifters and emoticons.  ... 
doi:10.1016/j.knosys.2016.05.032 fatcat:qtj4uxedqjh7fgdco22nz73d34

Tecnolengua Lingmotif at EmoInt-2017: A lexicon-based approach

Antonio Moreno-Ortiz
2017 Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis  
We based our intensity predictions for the four test datasets entirely on Lingmotif's TSS (text sentiment score) feature.  ...  We used the Lingmotif tool and a new, complementary tool, Lingmotif Learn, which we developed for this occasion.  ...  Generally speaking, lexiconbased approaches are preferred for sentence-level classification (Andreevskaia and Bergler, 2007) , whereas corpus-based, statistical approaches are preferred for document-level  ... 
doi:10.18653/v1/w17-5231 dblp:conf/wassa/Ortiz17 fatcat:nqz72rfrencjhcd62gy7mol3se

Sentiment Analysis of User-Generated Content on Drug Review Websites

Jin-Cheon Na, Wai Yan Min Kyaing
2015 Journal of Information Science Theory and Practice  
We are using 13 positive valence terms (e.g., help and improve) and 8 negative valence terms (e.g., hate and suffer) that are all verbs.  ...  Most of the early studies were focused on document-level analysis for assigning the sentiment orientation of a document (Pang et al., 2002) .  ... 
doi:10.1633/jistap.2015.3.1.1 fatcat:lpof4zu5cndevevhlq7ozvr6cy

Arabic Phrase-Level Contextual Polarity Recognition to Enhance Sentiment Arabic Lexical Semantic Database Generation

Samir E., Hanaa Mobarz, Ibrahim Farag, Mohsen Rashwan
2014 International Journal of Advanced Computer Science and Applications  
We then use the seminal English two-step contextual polarity phraselevel recognition approach to enhance word polarities within its contexts.  ...  Our results achieve significant improvement over baselines.  ...  may differ substantially from the sentence-level and the document-level in that resulting bag-of-words feature vectors tend to be very sparse resulting in lower classification accuracy [1] .  ... 
doi:10.14569/ijacsa.2014.051005 fatcat:nwafzxg67ffsvos3iaby7nbjwi

An Enhanced Technique for Analyzing Sentiments of Public Reviews - I

2019 International Journal of Inventive Engineering and Sciences  
The proposed system is to enhance the classification performance of the existing system by applying different classifiers apart from those used in existing system to obtain better results.  ...  The existing work on dual sentiment analysis includes techniques where dual training and dual prediction is performed.  ...  [6] proposed a method used for document level sentiment classification.  ... 
doi:10.35940/ijies.d0926.095619 fatcat:impneawuerczjc6dfg2zjwjqaq

Sentiment Analysis in Microblogs Using HMMs with Syntactic and Sentimental Information

Noo-Ri Kim, Kyoungmin Kim, Jee-Hyong Lee
2017 International Journal of Fuzzy Logic and Intelligent Systems  
We then build HMMs using the SIGs as hidden states for the initialization. The SIGs function as the prior knowledge of formative elements of sentimental sentences for HMMs.  ...  By using the SIGs, HMMs can start with informative hidden states and more precisely model the transition patterns of words in sentimental sentences with robust probability estimation.  ...  Next, we use the valence shifter list of Polanyi and Zaenen [24] to identify the contextual valence shifters. To determine the sentiment information of words, we use SentiWordNet [23] .  ... 
doi:10.5391/ijfis.2017.17.4.329 fatcat:itc5pnacnvc35ijeizdwux2doy

Learning-Based Method with Valence Shifters for Sentiment Analysis

Ruihua Cheng, Ji Meng Loh
2017 2017 IEEE International Conference on Data Mining Workshops (ICDMW)  
First, valence shifters and individual opinion words are combined as bigrams to use in an ordinal margin classifier.  ...  Automatic sentiment classification is becoming a popular and effective way to help online users or companies to process and make sense of customer reviews.  ...  The Enhanced System Adds Contextual Valence Shifters.  ... 
doi:10.1109/icdmw.2017.52 dblp:conf/icdm/ChengL17 fatcat:xicsu4ov6fe5xpedqheb73d7e4
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