Abstract
The opinion seeking behavior of people for good decision making has greatly enhanced the importance of social media as a platform for exchange of information.
This trend has led to a sudden spurt of information overflow on the Web.
The huge volume of such information has to be technically processed for segregating the relevant knowledge. Sentiment analysis is the popular method extensively used for this purpose.
It is defined as the computational study of mining the opinions from the available content about the entity of interest.
Existing Sentiment analysis techniques quite efficiently capture opinions from text written in syntactically correct and explicit language.
However, while dealing with the informal data, limitation has been observed in performance of sentiment analysis techniques.
With a view to deal with the imperfect and indirect language used by the netizens, it has become necessary to work on improvement in the existing sentiment analysis techniques.
In this regard, the conventional sentiment analysis techniques have shown some improvement on applying the appropriate context information.
However, still there is ample scope for further research to find the relevant “context” and applying it to a given scenario.
This systematic literature review paper intends to explore and analyze the existing work on the context-based sentiment analysis and to report gaps and future directions in the said research area.
REFERENCES
1. Aisopos F, Papadakis G, Tserpes K, Varvarigou T (2012) Content vs. context for sentiment analysis: a
comparative analysis over microblogs. In Proceedings of the 23rd ACM conference on Hypertext and social
media, pp. 187–196. ACM
2. Anjaria M, Guddeti RMR (2014) A novel sentiment analysis of social networks using supervised learning.
Soc Netw Anal Min 4(1):181
3. Bhatia MPS, Kumar A (2008) Information retrieval and machine learning: supporting technologies for web
mining research and practice. Webology 5(2):55
4. Bhatia MPS, Kumar A (2010) Paradigm shifts: from pre-web information systems to recent web-based
contextual information retrieval. Webology 1:7
5. Bosco C, Patti V, Bolioli A (2013) Developing corpora for sentiment analysis: the case of irony and senti-
tut. IEEE Intell Syst 28(2):55–63
6. Das S, Chen M (2001) Yahoo! For Amazon: extracting market sentiment from stock message boards. APFA 35:43
7. Deng S, Sinha AP, Zhao H (2017) Resolving ambiguity in sentiment classification: the role of dependency
features. ACM Trans Manag Inf Syst (TMIS) 8(2–3):4
8. Dey L, Mirajul Haque SK (2009) Opinion mining from noisy text data. Int J Doc Anal Recognit (IJDAR)
12(3):205–226
9. Dragoni M, Tettamanzi AGB, da Costa Pereira C (2015) Propagating and aggregating fuzzy polarities for
concept-level sentiment analysis. Cogn Comput 7(2):186–197
10. Feng S, Wang Y, Liu L et al. (2018) World Wide Web. https://doi.org/10.1007/s11280-018-0529-6
11. Fersini E, Pozzi FA, Messina E (2017) Approval network: a novel approach for sentiment analysis in social
networks. World Wide Web 20(4):831–854
12. Frankenstein W, Joseph K, Carley KM (2016) Contextual sentiment analysis. In: Xu K, Reitter D, Lee D,
Osgood N (eds) Social, cultural, and behavioral modeling. SBP-BRiMS 2016. Lecture notes in computer
science, vol 9708. Springer, Cham
13. Gaspar R, Pedro C, Panagiotopoulos P, Seibt B (2016) Beyond positive or negative: qualitative sentiment
analysis of social media reactions to unexpected stressful events. Comput Hum Behav 56:179–191
14. Gelli F, Uricchio T, Bertini M, Del Bimbo A, and Chang S-F (2015) Image popularity prediction in social
media using sentiment and context features. In Proceedings of the 23rd ACM international conference on
Multimedia, pp. 907–910. ACM
15. Han H, Bai X, Li P (2018) Neural Comput & Applic. https://doi.org/10.1007/s00521-018-3698-4
16. Hridoy SAA, Tahmid Ekram M, Islam MS, Ahmed F, Rahman RM (2015) Localized twitter opinion mining
using sentiment analysis. Decis Anal 2(1):8
17. Hung C (2017) Word of mouth quality classification based on contextual sentiment lexicons. Inf Process
Manag 53(4):751–763
18. Jiménez-Zafra SM, Montejo-Ráez A, Martin M, Lopez LAU (2017) "SINAI at SemEval-2017 Task 4: User
based classification." In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-
2017), pp. 634–639
19. Jurek A, Mulvenna MD, Bi Y (2015) Improved lexicon-based sentiment analysis for social media analytics.
Secur Inf 4(1):9
20. Kitchenham B, Brereton OP, Budgen D, Turner M, Bailey J, Linkman S Systematic literature reviews in
software engineering- a systematic literature review. Inf Softw Technol 51(1):7–15. https://doi.org/10.1016
/j.infsof.2008.09.009
21. Korenek P, Šimko M (2014) Sentiment analysis on microblog utilizing appraisal theory. World Wide Web
17(4):847–867
22. Kumar A, and Jaiswal A (2017) Empirical Study of Twitter and Tumblr for Sentiment Analysis using Soft
Computing Techniques. In Proceedings of the World Congress on Engineering and Computer Science, vol. 1
23. Kumar A, Sebastian TM (2012) Sentiment analysis on twitter. IJCSI Int J Comput Sci Issues 9(4):372
24. Kumar A, Teeja MS (2012) Sentiment analysis: a perspective on its past, present and future. Int J Intell Syst
Appl 4(10):1
25. Kumar A, Khorwal R, Chaudhary S (2016) A survey on sentiment analysis using swarm intelligence. Indian
J Sci Technol 9(39)
26. Kumar A, Dabas V, Hooda P (2018) Text classification algorithms for mining unstructured data: a SWOT
analysis. Int J Inf Technol. https://doi.org/10.1007/s41870-017-0072-1
27. Lau RYK, Li C, Liao SSY (2014) Social analytics: learning fuzzy product ontologies for aspect-oriented
sentiment analysis. Decis Support Syst 65:80–94
28. Li Y-M, Lin L, Chiu S-W (2014) Enhancing targeted advertising with social context endorsement. Int J
Electron Commer 19(1):99–128
29. Liu B (2010) Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing. Second
edition
30. Liu Y, Yu X, An A, Huang X (2013) Riding the tide of sentiment change: sentiment analysis with evolving
online reviews. World Wide Web 16(4):477–496
31. Majumder N, Hazarika D, Gelbukh A, Cambria E, Poria S (2018) Multimodal sentiment analysis using
hierarchical fusion with context modeling. Knowl-Based Syst 161:124–133
32. Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain
Shams Eng J 5(4):1093–1113
33. Meire M, Ballings M, Van den Poel D (2016) The added value of auxiliary data in sentiment analysis of
Facebook posts. Decis Support Syst 89:98–112
34. Muhammad A, Wiratunga N, Lothian R (2016) Contextual sentiment analysis for social media genres.
Knowl-Based Syst 108:92–101
35. Nakov P, Rosenthal S, Kiritchenko S, Mohammad SM, Kozareva Z, Ritter A, Stoyanov V, Zhu X (2016)
Developing a successful SemEval task in sentiment analysis of twitter and other social media texts. Lang
Resour Eval 50(1):35–65
36. Pan C (2018) Abnormal breast identification by nine-layer convolutional neural network with parametric
rectified linear unit and rank-based stochastic pooling. J Comput Sci 27:57–68
37. Pang B and Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr (1–2), 1–135
38. Pang, B, Lee L, and Vaithyanathan S (2002) "Thumbs up?: sentiment classification using machine learning
techniques." In Proceedings of the ACL-02 conference on Empirical methods in natural language
processing-Volume 10, pp. 79–86. Association for Computational Linguistics
39. Poria S, Cambria E, Winterstein G, Huang G-B (2014) Sentic patterns: dependency-based rules for concept-
level sentiment analysis. Knowl-Based Syst 69:45–63
40. Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and
applications. Knowl-Based Syst 89:14–46
41. Recupero DR, Presutti V, Consoli S, Gangemi A, Nuzzolese AG (2015) Sentilo: frame-based sentiment
analysis. Cogn Comput 7(2):211–225
42. Ren F, Ye W (2013) Predicting user-topic opinions in twitter with social and topical context. IEEE Trans
Affect Comput 4(4):412–424
43. Saif H, He Y, Fernandez M, Alani H (2016) Contextual semantics for sentiment analysis of twitter. Inf
Process Manag 52(1):5–19
44. Saif H, Fernandez M, Kastler L, Alani H (2017) Sentiment lexicon adaptation with context and semantics
for the social web. Semantic Web 8(5):643–665
45. Sheik, R, Philip SS, Sajeev A, Sreenivasan S, Jose G (2018) Entity level contextual sentiment detection of
topic sensitive influential twitterers using SentiCircles. In Data Engineering and Intelligent Computing, pp.
207–216. Springer, Singapore
46. Systematic Reviews: CRD's Guidance for Undertaking Reviews in Healthcare. by Jo Akers. Paperback, 292
Pages, Published 2009. ISBN-10: 1–900640–47-3 / 1900640473
47. Tao W, Liu T (2017) Building ontology for different emotional contexts and multilingual environment in
opinion mining. Intell Autom Soft Comput 1–7
48. Turney PD (2002) "Thumbs up or thumbs down?: semantic orientation applied to unsupervised classifica-
tion of reviews." In Proceedings of the 40th annual meeting on association for computational linguistics, pp.
417–424. Association for Computational Linguistics
49. Vanzo A, Croce D, Basili R (2014) A context based model for Sentiment Analysis in Twitter. In:
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics, pp.
2345–2354, Dublin City University and Association for Computational Linguistics, Dublin, Ireland, 2014
50. Vechtomova O (2017) Disambiguating context-dependent polarity of words: An information retrieval
approach. Inf Process Manag 53(5):1062–1079
51. Wang S (2018) Multiple sclerosis identification by 14-layer convolutional neural network with batch
normalization, dropout, and stochastic pooling. Front Neurosci 12:818
52. Wang S-H, Zhang Y, Li Y-J, Jia W-J, Liu F-Y, Yang M-M, Zhang Y-D (2016) Single slice based detection
for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based
optimization. Multimed Tools Appl 1–25
53. Weichselbraun A, Gindl S, Scharl A (2013) Extracting and grounding contextualized sentiment lexicons.
IEEE Intell Syst 28(2):39–46
54. Wiebe J, Wilson T, Bruce R, Bell M, Martin M (2004) Learning subjective language. Comput Linguist
30(3):277–308
55. Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase-level sentiment analysis.
In Proceedings of the conference on human language technology and empirical methods in natural language
processing, pp. 347–354. Association for Computational Linguistics
56. Wu F, Huang Y, Song Y (2016) Structured microblog sentiment classification via social context regular-
ization. Neurocomputing 175:599–609
57. Yang B, Cardie C (2014) Context-aware learning for sentence-level sentiment analysis with posterior
regularization. In ACL (1), pp. 325–335
58. Zhang Y (2018) Twelve-layer deep convolutional neural network with stochastic pooling for tea category
classification on GPU platform. Multimed Tools Appl 77(17):22821–22839
59. Zhang Y-D, Zhang Y, Hou X-X, Chen H, Wang S-H (2017) Seven-layer deep neural network based on
sparse autoencoder for voxelwise detection of cerebral microbleed. Multimed Tools Appl 1–18
60. Zhou Y, Lan M, Wu Y (2017) "ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine
Learning Methods for Twitter Message Polarity Classification." In Proceedings of the 11th International
Workshop on Semantic Evaluation (SemEval-2017), pp. 812–816
https://link.springer.com/article/10.1007%2Fs11042-019-7346-5
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