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Negation Handling in Sentiment Analysis at Sentence Level

Umar Farooq
2017 Journal of Computers  
Most of the existing sentiment analysis systems used traditional methods based on static window and punctuation marks to determine the scope of negation.  ...  One of the most important sub tasks in sentiment analysis is to determine the sequence of words affected by negation.  ...  Evaluation of Sentence Level Sentiment Analysis Conclusion One of the main reasons behind the errors in sentence level sentiment analysis is the inability to accurately determine the effect of negation  ... 
doi:10.17706/jcp.12.5.470-478 fatcat:dfln4qrikjdnbl5ruu3pzkaf2a

A Review on Negation Role in Twitter Sentiment Analysis

Itisha Gupta, Nisheeth Joshi
2021 International Journal of Healthcare Information Systems and Informatics  
The authors discuss the various approaches of modelling negation in Twitter sentiment analysis. In particular, their focus is on negation scope detection and negation handling methods.  ...  This article also presents some of the challenges and limits of negation accounting in the field of Twitter sentiment analysis.  ...  As negation might change the text polarity or strength of polarity (Weigand et al., 2010) , it is critical in sentiment analysis.  ... 
doi:10.4018/ijhisi.20211001.oa14 fatcat:ano35ozjb5chrfbrhz5cyxnk2a

Enhanced Twitter Sentiment Analysis Using Hybrid Approach and by Accounting Local Contextual Semantic

Itisha Gupta, Nisheeth Joshi
2019 Journal of Intelligent Systems  
Thus, we present naive and novel shift approach in which negation acts as both sentiment-bearing word and modifier, and then we shift the score of words from SWN based on their contextual semantic, inferred  ...  This paper addresses the problem of Twitter sentiment analysis through a hybrid approach in which SentiWordNet (SWN)-based feature vector acts as input to the classification model Support Vector Machine  ...  Hence, based on the dominant polarity of the affected word (under negation scope), we determine which role of negation to use (sentiment-bearing or modifier).  ... 
doi:10.1515/jisys-2019-0106 fatcat:5mtmmiiaavgdzp44qrdu4vjsem

Analyzing Sentiment in a Large Set of Web Data While Accounting for Negation [chapter]

Bas Heerschop, Paul van Iterson, Alexander Hogenboom, Flavius Frasincar, Uzay Kaymak
2011 Advances in Intelligent and Soft Computing  
As the role of negations in sentiment analysis has been explored only to a limited extent, we additionally investigate the impact of taking into account negation when analyzing sentiment.  ...  To this end, we utilize a basic sentiment analysis framework -consisting of a wordbank creation part and a document scoring part -taking into account negation.  ...  Acknowledgements We would like to thank Teezir (http://www.teezir.com) for their technical support, fruitful discussions, and for supplying us with data for this research.  ... 
doi:10.1007/978-3-642-18029-3_20 dblp:conf/awic/HeerschopIHFK11 fatcat:43sca2bzincldkdfxyrjdyn5lu

A machine-learning approach to negation and speculation detection for sentiment analysis

Noa P. Cruz, Maite Taboada, Ruslan Mitkov
2015 Journal of the Association for Information Science and Technology  
The resulting system works in two steps: in the first pass, negation/speculation cues are identified, and in the second phase the full scope of these cues is determined.  ...  The extrinsic evaluation shows that the correct identification of cues and scopes is vital for the task of sentiment analysis.  ...  Acknowledgments This work was supported in part by a grant to Maite Taboada from the Natural Sciences and Engineering Research Council of Canada (Discovery Grant 261104-2008).  ... 
doi:10.1002/asi.23533 fatcat:nmfcjwqoqvgidlbqrgidh2hnlq

Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach

Nicolas Pröllochs, Stefan Feuerriegel, Dirk Neumann
2019 Proceedings of the 2019 Conference of the North  
in the perceived negation scopes.  ...  Furthermore, an out-of-sample evaluation via sentiment analysis reveals consistent improvements (of up to 4.66 %) over both a sentiment analysis with (i) no negation handling and (ii) the use of word-level  ...  Performance in Sentiment Analysis.  ... 
doi:10.18653/v1/n19-1038 dblp:conf/naacl/ProllochsF019 fatcat:qjc6zzijsrfmnect2bka7nwdvy

Automatically Building Financial Sentiment Lexicons While Accounting for Negation

Thomas Bos, Flavius Frasincar
2021 Cognitive Computation  
In this research, we also propose two methods (Negated Word and Flip Sentiment) to extend the sentiment building approaches to take into account negation when constructing a sentiment lexicon.  ...  In addition, the created financial sentiment lexicons are compared with each other and with other existing sentiment lexicons.  ...  Acknowledgements We want to thank StockTwits for their help and for providing access to their back-up database.  ... 
doi:10.1007/s12559-021-09833-w fatcat:mexlughdujcglae55ljzrynzaa

Polarity Classification Tool for Sentiment Analysis in Malay Language

Normi Sham Awang Abu Bakar, Ros Aziehan Rahmat, Umar Faruq Othman
2019 IAES International Journal of Artificial Intelligence (IJ-AI)  
<p>The popularity of the social media channels has increased the interest among researchers in the sentiment analysis(SA) area.  ...  One aspect of the SA research is the determination of the polarity of the comments in the social media, i.e. positive, negative, and neutral.  ...  It helps the sentiment analysis tool to assign correct polarity when there are negation words in the text.  ... 
doi:10.11591/ijai.v8.i3.pp259-263 fatcat:eui43cf7lnfxjieavneqqqj5hy

Dual Sentiment Analysis with Three-Stage Model for Complex Polarity Shift Patterns with Two Sides of One Review

S. Vetrivel
2017 INTERNATIONAL JOURNAL OF EMERGING TRENDS IN SCIENCE AND TECHNOLOGY  
To deal with this problem, Dual Sentiment Analysis (DSA) is proposed in the recent work and it is used for SA classification.  ...  Bag-of-words (BOW) is now the major famous method to form text in numerical machine learning methods in Sentiment Analysis (SA).  ...  level of sentiment strength depending on learning from the corpus, and consequently determination still make sense in sentiment categorization.  ... 
doi:10.18535/ijetst/v4i8.36 fatcat:2faiy2gplfhsfbnjtpnfubcqga

Sentiment Analysis: An Overview from Linguistics

Maite Taboada
2016 Annual Review of Linguistics  
Descriptions of negation and their scope, and how it can be identified computationally, can be found in Saurí (2008) and Blanco and Moldovan (2013) .  ...  (Anonymous 2013) Assuming that negation and its scope have been adequately identified, the next problem is to decide how negation affects dictionary values for sentiment words.  ... 
doi:10.1146/annurev-linguistics-011415-040518 fatcat:madvbj6owncylecnizot3wzt6y

An Enhanced Technique for Analyzing Sentiments of Public Reviews - I

2019 International Journal of Inventive Engineering and Sciences  
Sentiment analysis is the process of extracting the opinion expressed in a piece of text to determine the writer's attitude towards a topic, product or any service in general and classify it into classes  ...  The existing work on dual sentiment analysis includes techniques where dual training and dual prediction is performed.  ...  [2] proposes a system for detecting the scope of negation to address the polarity shift problem.  ... 
doi:10.35940/ijies.d0926.095619 fatcat:impneawuerczjc6dfg2zjwjqaq

Contextual sentiment analysis for social media genres

Aminu Muhammad, Nirmalie Wiratunga, Robert Lothian
2016 Knowledge-Based Systems  
We use a text window size of three terms before and after a negation term to establish the scope of the negation.  ...  In SmartSA, the value of the dominant polarity of terms that are within the scope of the intensifier is increased (or decreased in the case of diminisher) relative to the strength of the A C C E P T  ... 
doi:10.1016/j.knosys.2016.05.032 fatcat:qtj4uxedqjh7fgdco22nz73d34

NRC-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets

Xiaodan Zhu, Svetlana Kiritchenko, Saif Mohammad
2014 Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)  
Key improvements over the 2013 systems are in the handling of negation. We create separate tweet-specific sentiment lexicons for terms in affirmative contexts and in negated contexts.  ...  These systems build on our SemEval-2013 sentiment analysis systems (Mohammad et al., 2013) which ranked first in both the termand message-level subtasks in 2013.  ...  Acknowledgments We thank Colin Cherry for providing his SVM code and for helpful discussions.  ... 
doi:10.3115/v1/s14-2077 dblp:conf/semeval/ZhuKM14 fatcat:ed6a4himtfcehcbixnh6mscxbi

Modality and Negation: An Introduction to the Special Issue

Roser Morante, Caroline Sporleder
2012 Computational Linguistics  
Researchers have started to work on modeling factuality, belief and certainty, detecting speculative sentences and hedging, identifying contradictions, and determining the scope of expressions of modality  ...  In this article, we will provide an overview of how modality and negation have been modeled in computational linguistics.  ...  We follow here the definitions of Pang and Lee (2008) who use "opinion mining" and "sentiment analysis" as largely synonymous terms and "subjectivity analysis" as a cover term for both. determine the strength  ... 
doi:10.1162/coli_a_00095 fatcat:6p6vlzsrnfglve7fupa5ahykmu

A hybrid approach to sentiment analysis

Orestes Appel, Francisco Chiclana, Jenny Carter, Hamido Fujita
2016 2016 IEEE Congress on Evolutionary Computation (CEC)  
This contribution presents a hybrid approach to Sentiment Analysis (SA) encompassing the use of semantic rules, fuzzy sets, unsupervised machine learning techniques and a sentiment lexicon improved with  ...  The mechanism of the new SA methodology is illustrated by applying it to compute the polarity of a given sentence and to a benchmarking publicly available dataset: the Movie Review Dataset.  ...  The rest of this article will present our proposed Hybrid Method for Sentiment Analysis covering its three main components, namely: (i) the Sentiment/Opinion Lexicon, (ii) Negation handling and Semantic  ... 
doi:10.1109/cec.2016.7744425 dblp:conf/cec/AppelCCF16 fatcat:dsxar7hkzneinb2uvho76glxiu
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