Sentiment Analysis and Opinion Mining from Social Media : A Review

Sentiment Analysis and Opinion Mining from Social Media : A Review

Savitha Mathapati

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S H Manjula

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Venugopal K R

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Bangalore University

Sentiment Analysis and Opinion Mining from Social Media : A Review

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Abstract

Ubiquitous presence of internet, advent of web 2.0 has made social media tools like blogs, Facebook, Twitter very popular and effective. People interact with each other, share their ideas, opinions, interests and personal information. These user comments are used for finding the sentiments and also add financial, commercial and social values. However, due to the enormous amount of user generated data, it is an expensive process to analyze the data manually. Increase in activity of opinion mining and sentiment analysis, challenges are getting added every day. There is a need for automated analysis techniques to extract sentiments and opinions conveyed in the usercomments. Sentiment analysis, also known as opinion mining is the computational study of sentiments and opinions conveyed in natural language for the purpose of decision making.

References

101 Cites in Article

Reference Format

G Vasanthakumar,Bagul Prajakta,P Shenoy,K Venugopal,L Patnaik (2016). PIB: Profiling Influential Blogger in Online Social Networks, A Knowledge Driven Data Mining Approach.
N Godbole,M Srinivasaiah,S Skiena (2007). Large-Scale Sentiment Analysis for News and Blogs.
S Li,C.-R Huang,G Zhou,S Lee (2010). Employing Personal / impersonal Views in Supervised and Semi-supervised Sentiment Classification.
J Jiang,C Zhai Instance Weighting for Domain Adaptation in NLP.
H Daum (2009). e III, Frustratingly Easy Domain Adaptation.
S Pan,J Kwok,Q Yang (2008). Transfer Learning via Dimensionality Reduction.
R Ando,T Zhang (2005). A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data.
John Blitzer,Ryan Mcdonald,Fernando Pereira (2006). Domain adaptation with structural correspondence learning.
J Blitzer,M Dredze,F Pereira (2007). Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification.
S Pan,X Ni,J.-T Sun,Q Yang,Z Chen (2010). Cross-domain Sentiment Classification via Spectral Feature Alignment.
G Vasanthakumar,P Shenoy,K Venugopal (2015). PFU: Profiling Forum users in online social networks, a knowledge driven data mining approach.
D Bollegala,D Weir,J Carroll (2011). Using Multiple Sources to Construct a Sentiment Sensitive The saurus for Cross-domain Sentiment Classification.
Shai Ben-David,John Blitzer,Koby Crammer,Alex Kulesza,Fernando Pereira,Jennifer Vaughan (2010). A theory of learning from different domains.
X Shi,Q Liu,W Fan,P Yu,R Zhu (2010). Transfer Learning on Feature Spaces via Spectral Transformation.
Masashi Sugiyama,Taiji Suzuki,Shinichi Nakajima,Hisashi Kashima,Paul Von Bünau,Motoaki Kawanabe (2008). Direct importance estimation for covariate shift adaptation.
Hidetoshi Shimodaira (2000). Improving predictive inference under covariate shift by weighting the log-likelihood function.
M Chen,K Weinberger,J Blitzer (2011). Cotraining for Domain Adaptation.
Gokhan Tur (2009). Co-adaptation: Adaptive co-training for semi-supervised learning.
A Kumar,H Saha,Daume (2010). Coregularization based Semi-supervised Domain Adaptation.
J Blitzer,S Kakade,D Foster (2011). Domai Adaptation with Coupled Subspaces.
Kate Saenko,Brian Kulis,Mario Fritz,Trevor Darrell (2010). Adapting Visual Category Models to New Domains.
Brian Kulis,Kate Saenko,Trevor Darrell (2011). What you saw is not what you get: Domain adaptation using asymmetric kernel transforms.
C Wang,S Mahadevan (2011). Heterogeneous Domain Adaptation using Manifold Alignment.
W Dai,Y Chen,G.-R Xue,Q Yang,Y Yu (2008). Translated Learning: Transfer Learning across Different Feature Spaces.
Yejin Choi,Claire Cardie (2009). Adapting a polarity lexicon using integer linear programming for domain-specific sentiment classification.
Veselin Stoyanov,Claire Cardie (2008). Topic identification for fine-grained opinion analysis.
Y He,C Lin,H Alani (2011). Automatically Extracting Polarity-bearing Topics for Cross-domain Sentiment Classification.
S Gao,H Li (2011). A Cross-domain Aadaptation Method for Sentiment Classification using Probabilistic Latent Analysis.
Veselin Stoyanov,Claire Cardie (2008). Topic identification for fine-grained opinion analysis.
D Das,S Bandyopadhyay (2013). Emotion Coreferencing-Emotional Expression, Holder, and Topic.
F Li,S Wang,S Liu,M Zhang (2014). Suit: A Supervised User-item based Topic Model for Sentiment Analysis.
Yelena Mejova,Padmini Srinivasan (2012). Crossing Media Streams with Sentiment: Domain Adaptation in Blogs, Reviews and Twitter.
Dipankar Das,Sivaji Bandyopadhyay (2010). Extracting emotion topics from blog sentences.
Shenghua Liu,Fuxin Li,Fangtao Li,Xueqi Cheng,Huawei Shen (2013). Adaptive co-training SVM for sentiment classification on tweets.
A Agarwal,B Xie,I Vovsha,O Rambow,R Passonneau (2011). Sentiment Analysis of Twitter Data.
Shenghua Liu,Wenjun Zhu,Ning Xu,Fangtao Li,Xue-Qi Cheng,Yue Liu,Yuanzhuo Wang (2013). Co-training and visualizing sentiment evolvement for tweet events.
Andranik Tumasjan,Timm Sprenger,Philipp Sandner,Isabell Welpe (2010). Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment.
Dipankar Das,Sivaji Bandyopadhyay (2010). Identifying emotion topic — An unsupervised hybrid approach with Rhetorical Structure and Heuristic Classifier.
Y Kim,S Jeong (2015). Opinion-Mining Methodology for Social Media Analytics.
H Yu (2010). Structure-aware Review Mining and Summarization.
T Ma,X Wan (2010). Opinion Target Extraction in Chinese News Comments.
Qi Zhang,Yuanbin Wu,Tao Li,Mitsunori Ogihara,Joseph Johnson,Xuanjing Huang (2009). Mining product reviews based on shallow dependency parsing.
Wei Jin,Hung Ho (2009). A novel lexicalized HMM-based learning framework for web opinion mining.
F Li,S Pan,O Jin,Q Yang,X Zhu (2012). Crossdomain Co-extraction of Sentiment and Topic Lexicons.
M Hu,B Liu (2004). Mining Opinion Features in Customer Reviews.
G Qiu,B Liu,J Bu,C Chen (2011). Opinion Word Expansion and Target Extraction Through Double Propagation.
K Liu,L Xu,J Zhao (2012). Opinion Target Extraction using Word-based Translation Model.
W Zhao,J Jiang,H Yan,X Li (2010). Jointly Modeling Aspects and Opinions with a Maxent-LDA hybrid.
B Mukherjee,Liu (2012). Modeling Review Comments.
Vandana Jha,N Manjunath,P Shenoy,K Venugopal,L Patnaik (2015). HOMS: Hindi opinion mining system.
Bo Pang,Lillian Lee,Shivakumar Vaithyanathan (2002). Thumbs up?.
K Bloom,N Garg,S Argamon (2007). Demonstration Papers at HLT-NAACL 2004 on XX - HLT-NAACL '04.
Janyce Wiebe,Theresa Wilson,Rebecca Bruce,Matthew Bell,Melanie Martin (2004). Learning Subjective Language.
L Vibha,G Harshavardhan,K Pranaw,P Shenoy,K Venugopal,L Patnaik (2006). Classification of Mammograms using Decision Trees.
Vasanthakumar G U,Aakriti Upadhyay,Pradeep Kalmath,Sthita Dinakar,P Shenoy,Venugopal K R (2015). UP3: User profiling from Profile Picture in Multi-Social Networking.
M Hu,B Liu (2006). Opinion Feature Extraction Using Class Sequential Rules.
S.-M Kim,E Hovy (2006). Extracting Opinions, Opinion Holders, and Topics Expressed in Online News Media Text.
Changbo Wang,Zhao Xiao,Yuhua Liu,Yanru Xu,Aoying Zhou,Kang Zhang (2013). SentiView: Sentiment Analysis and Visualization for Internet Popular Topics.
G Li,S Hoi,K Chang,W Liu,R Jain (2014). Collaborative Online Multitask Learning.
X Chen,M Vorvoreanu,K Madhavan (2014). Mining Social Media Data for Understanding Students' Learning Experiences.
S Tan,Y Li,H Sun,Z Guan,X Yan,J Bu,C Chen,X He (2014). Interpreting the Public Sentiment Variations on Twitter.
Eduard Dragut,Hong Wang,Prasad Sistla,Clement Yu,Weiyi Meng (2012). Polarity Consistency Checking for Domain Independent Sentiment Dictionaries.
G Vasanthakumar,Bagul Prajakta,P Shenoy,K Venugopal,L Patnaik (2015). PIB: Profiling Influential Blogger in Online Social Networks, A Knowledge Driven Data Mining Approach.
Soo-Min Kim,Eduard Hovy (2006). Identifying and analyzing judgment opinions.
Delip Rao,Deepak Ravichandran (2009). Semi-supervised polarity lexicon induction.
V Jha,R Savitha,S Hebbar,P Shenoy,K Venugopal (2015). HMDSAD: Hindi Multi-domain Sentiment Aware Dictionary.
A Hassan,D Radev (2010). Identifying Text Polarity using Random Walks.
Alistair Kennedy,Diana Inkpen (2006). SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS.
Shichen Li,Zhongqing Wang,Xiaotong Jiang,Guodong Zhou (2010). Cross-Domain Sentiment Classification using Semantic Representation.
T Wilson,J Wiebe,P Hoffmann (2009). Recognizing Contextual Polarity: An Exploration of Features for Phrase-level Sentiment Analysis.
T Nakagawa,K Inui,S Kurohashi (2010). Dependency Tree-based Sentiment Classification using CRF s with Hidden Variables.
X Ding,B Liu (2007). The Utility of Linguistic Rules in Opinion Mining.
X Ding,B Liu,P Yu (2008). A Holistic Lexiconbased Approach to Opinion Mining.
Xiaowen Ding,Bing Liu,Lei Zhang (2009). Entity discovery and assignment for opinion mining applications.
Peter Turney (2002). Thumbs up or thumbs down?.
P Turney,M Littman (2003). Measuring Praise and Criticism: Inference of Semantic Orientation Year 2016 ( ) C Sentiment Analysis and Opinion Mining from Social Media : A Review from Association.
Yejin Choi,Claire Cardie (2008). Learning with compositional semantics as structural inference for subsentential sentiment analysis.
E Agirre,D Martinez (2000). Exploring Automatic Word Sense Disambiguation with Decision Lists and the Web.
Sanae Fujita,Akinori Fujino (2013). Word Sense Disambiguation by Combining Labeled Data Expansion and Semi-Supervised Learning Method.
Rui Xia,Feng Xu,Chengqing Zong,Qianmu Li,Yong Qi,Tao Li (2015). Dual Sentiment Analysis: Considering Two Sides of One Review.
N Jakob,I Gurevych (2010). Extracting Opinion Targets in a Single-and Cross-domain Setting with Conditional Random Fields.
Guang Qiu,Bing Liu,Jiajun Bu,Chun Chen (2011). Opinion Word Expansion and Target Extraction through Double Propagation.
V Hatzivassiloglou,J Wiebe (2000). Effects of Adjective Orientation and Gradability on Sentence Subjectivity.
V Jha,N Manjunath,P Shenoy,K Venugopal (2015). HSAS: Hindi Subjectivity Analysis System.
B Pang,L Lee,S Vaithyanathan (2002). Thumbs up?: Sentiment Classification using Machine Learn-ing Techniques.
L Vibha,G Harshavardhan,K Pranaw,P Shenoy,K Venugopal,L Patnaik (2007). Classification of Mammograms Using Decision Trees.
B Pang,L Lee (2004). A Sentimental Education: Sentiment Analysis using Subjectivity Summarization based on Minimum Cuts.
Janyce Wiebe,Rebecca Bruce,Thomas O'hara (1999). Development and use of a gold-standard data set for subjectivity classifications.
E Riloff,J Wiebe (2003). Learning Extraction Pat-terns for Subjective Expressions.
K Srinivasa,A Singh,A Thomas,K Venugopal,L Patnaik (2005). Generic Feature Extraction for Classification using Fuzzy C-means Clustering.
D Sejal,V Rashmi,K Venugopal,S Iyengar,L Patnaik (2016). Image Recommendation based on Keyword Relevance using Absorbing Markov Chain and Image Features.
G Adomavicius,A Tuzhilin (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions.
Marco Vanetti,E Binaghi,E Ferrari,B Carminati,M Carullo (2013). A System to Filter Unwanted Messages from OSN User Walls.
S Zelikovitz,H Hirsh (2000). Improving Short Text Classification using Unlabeled Background Knowledge to Assess Document Similarity.
B Sriram,D Fuhry,E Demir,H Ferhatosmanoglu,M Demirbas (2010). Short Text Classification in Twitter to Improve Information Filtering.
Jennifer Golbeck (2006). Combining Provenance with Trust in Social Networks for Semantic Web Content Filtering.
Marco Vanetti,Elisabetta Binaghi,Barbara Carminati,Moreno Carullo,Elena Ferrari (2010). Content-Based Filtering in On-Line Social Networks.
Moreno Carullo,Elisabetta Binaghi,Ignazio Gallo (2009). An online document clustering technique for short web contents.
L Duan,D Xu,I Tsang (2014). Learning with Augmented Features for Heterogeneous Domain Adaptation.
J Cao,K Zeng,H Wang,J Cheng,F Qiao,D Wen,Y Gao (2014). Web-Based Traffic Sentiment Analysis: Methods and Applications.
Zhen Hai,Kuiyu Chang,Jung-Jae Kim,Christopher Yang (2014). Identifying Features in Opinion Mining via Intrinsic and Extrinsic Domain Relevance.

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No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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How to Cite This Article

Savitha Mathapati. 2017. \u201cSentiment Analysis and Opinion Mining from Social Media : A Review\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C5): .

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GJCST Volume 16 Issue C5
Pg. 77- 90

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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H.2.8,J.4

Submission ReceivedDecember 10, 2015
Peer Review Double Blind
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Accepted January 1, 2016
Published January 15, 2016

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January 27, 2017

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Article Matrices

Total Score: 103

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Savitha Mathapati, S H Manjula, Venugopal K R (PhD/Dr. count: 0)

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