Sentiment Analysis(SA) is a way of analyzing the sentiment/emotion in the sentence. Entity, sentence or document- level sentences have been carried out to discover the polarity of text. Sentiment is measured using various approaches such as lexical-based and supervised machine learning methods. This survey focuses on various methods for Feature Extraction and Emotion Detection. Before applying any algorithm for sentiment polarity identification, preprocessing is to be carried over. Machine learning(ML) techniques such as Naive Bayes, SVM, and Max Entropy have been applied to identify the polarity score that are classified as positive, negative or neutral. Feature Selection method identifies a subset of most functional features from the entire set of features. The major challenge in Opinion mining lies in identifying the emotion expressed in the text. This survey provides an insight into the efficient techniques, methods and future scope in opinion mining investigation.

Sentiment Analysis; Support Vector Machine; Emotion Detection; Feature Selection; Machine Learning

[1] W. Che, Y. Zhao, H. Guo, Z. Su, and T. Liu, “Sentence Compression for Aspect-Based Sentiment Analysis,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 23, no. 12, pp. 2111–2124, 2015. 

[2] D. Sejal, V. Rashmi, K. R. Venugopal, S. S. Iyengar, and L. M. Patnaik, “Image Recommendation based on Keyword Relevance using Absorbing Markov Chain and Image Features,” International Journal of Multimedia Information Retrieval, vol. 5, no. 3, pp. 185–199, 2016. 

[3] K. C. Srikantaiah, N. K. Kumar, K. R. Venugopal, and L. M. Patnaik, “Web Caching and Prefetching with Cyclic Model Analysis of Web Object Sequences,” International Journal of Knowledge and Web Intelligence 2, vol. 5, no. 1, pp. 76–103, 2014. 

[4] R. H. Vishwanath, S. V. Leena, K. C. Srikantaiah, S. Kumar, P. D. Shenoy, K. R. Venugopal, and L. M. Patnaik, “APST: Approximation and Prediction of Stock Time-Series Data using Pattern Sequence,” International Conference on Data Mining Workshops, 2013. 

[5] G. U. Vasanthakumar, B. Prajakta, P. D. Shenoy, K. R. Venugopal, and L. M. Patnaik, “PIB: Profiling Influential Blogger in Online Social Networks, A Knowledge Driven Data Mining Approach,” Procedia Computer Science, vol. 54, pp. 362–370, 2015. 

[6] B. Liu, “Sentiment Analysis and Opinion Mining,” Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1–167, 2012. 

[7] G. Wang, J. Sun, J. Ma, K. Xu, and J. Gu, “Sentiment Classification: 
The Contribution of Ensemble Learning,” Decision support systems, 
vol. 57, pp. 77–93, 2014. 

[8] G. Vinodhini and R. M. Chandrasekaran, “Sentiment Analysis and 
Opinion Mining: A Survey,” International Journal of Advanced Re- search in Computer Science and Software Engineering, vol. 2, no. 6, pp. 282–292, 2012. 

[9] D. Tang, B. Qin, F. Wei, L. Dong, T. Liu, and M. Zhou, “A Joint Segmentation and Classification Framework for Sentence Level Sentiment Classification,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 23, no. 11, pp. 1750–1761, 2015.
[10] A. Jindal, A. Dua, K. Kaur, M. Singh, N. Kumar, and S.
Mishra, “Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid,” IEEE Transactions on Industrial Informatics, vol. 12, no. 3, pp. 1005–1016, 2016.
[11] M. Gamon, “Sentiment Classification on Customer Feedback Data: Noisy Data, Large Feature Vectors and the Role of Linguistic Analysis,” Proceedings of the 20th international conference on Computational Linguistics, pp. 841–847, 2004.
[12] X. Zhou, X. Wan, and J. Xiao, “CMiner: Opinion Extraction and Summarization for Chinese Microblogs,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 7, pp. 1650–1663, 2016.
[13] Q. Ye, Z. Zhang, and R. Law, “Sentiment Classification of Online Reviews to travel Destinations by Supervised Machine Learning Approaches,” Expert Systems with Applications, vol. 36, no. 3, pp. 6527– 6535, 2009.
[14] G. Laboreiro, L. Sarmento, J. Teixeira, and E. Oliveira, “Tokenizing Micro-Blogging Messages using a Text Classification Approach,” Proceedings of the Fourth Workshop on Analytics for Noisy Unstructured Text Data, pp. 81–88, 2010.
[15] M. R. Saleh, M. T. Mart ́ın-Valdivia, A. Montejo-Ra ́ez, and L. Uren ̃a- Lo ́pez, “Experiments with SVM to Classify Opinions in Different Domains,” Expert Systems with Applications, vol. 38, no. 12, pp. 14 799–14 804, 2011.
[16] R. Moraes, J. F. Valiati, and W. P. G. Neto, “Document-Level Sentiment Classification: An Empirical Comparison between SVM and ANN,” Expert Systems with Applications, vol. 40, no. 2, pp. 621–633, 2013.
[17] S. M. Mohammad, S. Kiritchenko, and X. Zhu, “NRC-Canada: Building the state-of-the-art in Sentiment Analysis of Tweets,” Seventh International Workshop on Semantic Evaluation Exercises (SemEval- 2013), 2013.
[18] S. Liu, X. Cheng, F. Li, and F. Li, “TASC: Topic-Adaptive Sentiment Classification on Dynamic Tweets,” IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 6, pp. 1696–1709, 2015.
[19] J. Smailovic ́, M. Grcˇar, N. Lavracˇ, and M. Zˇnidarsˇicˇ, “Stream-Based Active Learning for Sentiment Analysis in the Financial Domain,” International Journal of Information Sciences, vol. 285, pp. 181–203, 2014.
[20] S. Canuto, M. A. Goncalves, and F. Benevenuto, “Exploiting New Sentiment-Based Meta-Level Features for Effective Sentiment Analysis,” Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 53–62, 2016.
[21] D. D. Wu, L. Zheng, and D. L. Olson, “A Decision Support Approach for Online Stock Forum Sentiment Analysis,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 44, no. 8, pp. 1077–1087, 2014.
[22] A. Sharma and S. Dey, “A Document-Level Sentiment Analysis Approach using Artificial Neural Network and Sentiment Lexicons,” ACM SIGAPP Applied Computing Review, vol. 12, no. 4, pp. 67–75, 2012.
[23] X. Wang, F. Wei, X. Liu, M. Zhou, and M. Zhang, “Topic Sentiment Analysis in Twitter: A Graph-Based Hashtag Sentiment Classification Approach,” Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1031–1040, 2011.
[24] M. Trupthi, S. Pabboju, and G. Narasimha, “Improved Feature Ex- traction and Classification Sentiment Analysis,” International Conference on Advances in Human Machine Interaction (HMI), pp. 1–6, 2016.
[25] R. Yao and J. Chen, “Predicting Movie Sales Revenue using Online Re- views,” IEEE International Conference on Granular Computing (GrC), pp. 396–401, 2013.
[26] P. Bhoir and S. Kolte, “Sentiment Analysis of Movie Reviews using Lexicon Approach,” IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–6, 2015.
[27] A. Ekawijana and H. Heryono, “Composite Naive Bayes Classification and Semantic method to enhance Sentiment Accuracy Score,” 4th International Conference on Cyber and IT Service Management, pp. 1–4, 2016.
[28] V. Jha, N. Manjunath, P. D. Shenoy, K. Venugopal, and L. Patnaik, “HOMS: Hindi Opinion Mining System,” IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), pp. 366– 371, 2015.
[29] R. Jose and V. S. Chooralil, “Prediction of Election Result by Enhanced Sentiment Analysis on Twitter Data using Classifier Ensemble Approach,” International Conference on Data Mining and Advanced Computing (SAPIENCE), pp. 64–67, 2016.
[30] M. A. M. Raja, S. G. Winster, and S. Swamynathan, “Review Analyzer: Analyzing Consumer Product Reviews from Review Collections,” International Conference on Recent Advances in Computing and Software Systems (RACSS), pp. 287–292, 2012.
[31] P. Andriotis, G. Oikonomou, T. Tryfonas, and S. Li, “Highlighting Relationships of a Smartphone’s Social Ecosystem in Potentially Large Investigations,” IEEE Transactions on Cybernetics, vol. 46, no. 9, pp. 1974–1985, 2016.
[32] C. Wang, Z. Xiao, Y. Liu, Y. Xu, A. Zhou, and K. Zhang, “SentiView: Sentiment Analysis and Visualization for Internet Populars,” IEEE Transactions on Human-Machine Systems, vol. 43, no. 6, pp. 620–630, 2013.
[33] C. C. Yang and T. D. Ng, “Analyzing and Visualizing Web Opinion Development and Social Interactions with Density-Based Clustering,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 41, no. 6, pp. 1144–1155, 2011.
[34] C. Troussas, M. Virvou, and S. Mesaretzidis, “Comparative Analysis of Algorithms for Student Characteristics Classification using a Methodological Framework,” 6th International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–5, 2015. 

[35] X. Chen, M. Vorvoreanu, and K. Madhavan, “Mining Social Media Data for understanding Students Learning Experiences,” IEEE Trans actions on Learning Technologies, vol. 7, no. 3, pp. 246–259, 2014. 

[36] E. Haddi, X. Liu, and Y. Shi, “The Role of Text Pre-Processing in Sentiment Analysis,” Procedia Computer Science, vol. 17, pp. 26–32, 2013. 

[37] G. Liu, X. Xu, B. Deng, S. Chen, and L. Li, “A Hybrid method for Bilingual Text Sentiment Classification based on Deep Learning,” 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/ Distributed Computing (SNPD), pp. 93–98, 2016. 

[38] N. F. Da Silva, E. R. Hruschka, and E. R. Hruschka, “Tweet Sentiment Analysis with Classifier Ensembles,” International Journal of Decision Support Systems, vol. 66, pp. 170–179, 2014. 

[39] F. H. Khan, S. Bashir, and U. Qamar, “TOM: Twitter Opinion Mining Framework using Hybrid Classification Scheme,” International Journal of Decision Support Systems, vol. 57, pp. 245–257, 2014. 

[40] L. Shi, B. Sun, L. Kong, and Y. Zhang, “Web Forum Sentiment Analysis based on Topics,” Ninth IEEE International Conference on Computer and Information Technology, 2009. CIT’09, vol. 2, pp. 148– 153, 2009.
[41] D. Tang, F. Wei, N. Yang, M. Zhou, T. Liu, and B. Qin, “Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification,” Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 1555–1565, 2014. 

[42] J. Yang, Y. Liu, X. Zhang, X. Yuan, Y. Zhao, S. Barlowe, and S. Liu, “Piwi: Visually Exploring Graphs based on their Community Structure,” IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 6, pp. 1034–1047, 2013.
[43] R. Xia, C. Zong, and S. Li, “Ensemble of Feature Sets and Classification Algorithms for Sentiment Classification,” International Journal of Information Sciences, vol. 181, issue. 6, pp. 1138–1152, 2011.
[44] L. Jiang, M. Yu, M. Zhou, X. Liu, and T. Zhao, “Target-Dependent Twitter Sentiment Classification,” Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 151–160, 2011. 

[45] M. Sokolova and G. Lapalme, “A Systematic Analysis of Performance Measures for Classification Tasks,” International Journal of Information Processing & Management, vol. 45, no. 4, pp. 427–437, 2009.
[46] D. T. Vo and Y. Zhang, “Target-Dependent Twitter Sentiment Classification with Rich Automatic Features,” Proceedings of the Twenty- Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015), pp. 1347–1353, 2015. 

[47] M. Speriosu, N. Sudan, S. Upadhyay, and J. Baldridge, “Twitter Polarity Classification with Label Propagation over Lexical Links and the Follower Graph,” Proceedings of the First workshop on Unsupervised Learning in NLP, pp. 53–63, 2011.
[48] Y. Hu and W. Li, “Document Sentiment Classification by Exploring Description Model of Topical Terms,” International Journal of Computer Speech & Language, vol. 25, issue. 2, pp. 386–403, 2011. 

[49] Y. Zhang, M. J. Er, R. Venkatesan, N. Wang, and M. Pratama, “Sentiment Classification using Comprehensive Attention Recurrent models,” International Joint Conference on Neural Networks (IJCNN), pp. 1562–1569, 2016.
[50] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, “Lexicon- Based Methods for Sentiment Analysis,” Computational linguistics, vol. 37, issue. 2, pp. 267–307, 2011.
[51] D. Davidov, O. Tsur, and A. Rappoport, “Enhanced Sentiment Learning using Twitter Hashtags and Smileys,” Proceedings of the 23rd International Conference on Computational Linguistics: posters, pp. 241–249, 2010. 

[52] A. Celikyilmaz, R. Sarikaya, M. Jeong, and A. Deoras, “An Empirical Investigation of Word Class-Based Features for Natural Language Understanding,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 24, no. 6, pp. 994–1005, 2016. 

[53] A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning Word Vectors for Sentiment Analysis,” Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol 1, pp. 142–150, 2011. 

[54] E. C. Dracut, H. Wang, P. Sistla, C. Yu, and W. Meng, “Polarity Consistency Checking for Domain Independent Sentiment Dictionaries,” IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 3, pp. 838–851, 2015.

[55] W.-F. Chen, M.-H. Chen, M.-L. Chen, and L.-W. Ku, “A Computer- Assistance Learning System for Emotional Wording,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 5, pp. 1093– 1104, 2016.
[56] D. Tang, F. Wei, B. Qin, N. Yang, T. Liu, and M. Zhou, “Sentiment Embeddings with Applications to Sentiment Analysis,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 2, pp. 496–509, 2016.
[57] R. P. Schumaker, Y. Zhang, C.-N. Huang, and H. Chen, “Evaluating Sentiment in Financial News Articles,” International Journal of Decision Support Systems, vol. 53, issue. 3, pp. 458–464, 2012.
[58] F. Ren and Y. Wu, “Predicting User-Topic Opinions in Twitter with Social and Topical Context,” IEEE Transactions on Affective Computing, vol. 4, no. 4, pp. 412–424, 2013.
[59] J. Yi, T. Nasukawa, R. Bunescu, and W. Niblack, “Sentiment Analyzer: Extracting Sentiments about a given Topic using Natural Language Processing Techniques,” Third IEEE International Conference on Data Mining, pp. 427–434, 2003.
[60] M. Z. Asghar, A. Khan, S. Ahmad, and F. M. Kundi, “A Review of Feature Extraction in Sentiment Analysis,” Journal of Basic and Applied Scientific Research, vol. 4, no. 3, pp. 181–186, 2014.
[61] A. Celikyilmaz, R. Sarikaya, M. Jeong, and A. Deoras, “An Empirical Investigation of Word Class-Based Features for Natural Language Understanding,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 24, no. 6, pp. 994–1005, 2016.
[62] S. Poria, E. Cambria, N. Howard, G.-B. Huang, and A. Hussain, “Fusing Audio, Visual and Textual Clues for Sentiment Analysis from Multimodal Content,” Neurocomputing, vol. 174, pp. 50–59, 2016.
[63] B. Agarwal and N. Mittal, “Prominent Feature Extraction for Review Analysis: An Empirical Study,” Journal of Experimental & Theoretical Artificial Intelligence, vol. 28, no. 3, pp. 485–498, 2016.
[64] S. Pasarate and R. Shedge, “Comparative Study of Feature Extraction Techniques used in Sentiment Analysis,” 2016 International Conference on Innovation and Challenges in Cyber Security (ICICCS- INBUSH), pp. 182–186, 2016.
[65] A. Duric and F. Song, “Feature Selection for Sentiment Analysis based on Content and Syntax Models,” International Journal of Decision Support Systems, vol. 53, no. 4, pp. 704–711, 2012.
[66] F. Li, S. J. Pan, O. Jin, Q. Yang, and X. Zhu, “Cross-Domain co-Extraction of Sentiment and Topic Lexicons,” Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Paper, vol 1, pp. 410–419, 2012.
[67] H. Uguz, “A Two-Stage Feature Selection Method for Text Categorization by using Information Gain, Principal Component Analysis and Genetic Algorithm,” International Journal of Knowledge-Based Systems, vol. 24, issue. 7, pp. 1024–1032, 2011.
[68] R. M. Elawady, S. Barakat, and N. M. Elrashidy, “Different Feature Selection for Sentiment Classification,” International Journal of Information Science and Intelligent System, vol. 3, no. 1, pp. 137–150, 2014.
[69] A. Abbasi, H. Chen, and A. Salem, “Sentiment Analysis in Multiple Languages: Feature Selection for Opinion Classification in Web Forums,” ACM Transactions on Information Systems (TOIS), vol. 26, no. 3, pp. 1–34, 2008.
[70] B. Agarwal, S. Poria, N. Mittal, A. Gelbukh, and A. Hussain, “Concept- Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach,” International Journal of Cognitive Computation, vol. 7, no. 4, pp. 487–499, 2015.
[71] Y. Matsuyama, A. Saito, S. Fujie, and T. Kobayashi, “Automatic Expressive Opinion Sentence Generation for Enjoyable Conversational Systems,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 23, no. 2, pp. 313–326, 2015.
[72] N. Shelke, S. Deshpande, and V. Thakare, “Domain Independent Approach for Aspect Oriented Sentiment Analysis for Product Reviews,” Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications, pp. 651–659, 2017.
[73] H. Wang, L. Liu, W. Song, and J. Lu, “Feature-Based Sentiment Analysis Approach for Product Reviews,” Journal of Software, vol. 9, no. 2, pp. 274–279, 2014.
[74] F. Xianghua, L. Guo, G. Yanyan, and W. Zhiqiang, “Multi-Aspect Sentiment Analysis for Chinese Online Social Reviews based on Topic Modeling and HowNet Lexicon,” International Jouranl of Knowledge-Based Systems, vol. 37, pp. 186–195, 2013.
[75] Z. Yu, Z. Yu, X. Zhou, C. Becker, and Y. Nakamura, “Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings,” IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 4, pp. 759–768, 2012.
[76] W. Rong, Y. Nie, Y. Ouyang, B. Peng, and Z. Xiong, “Auto-Encoder 
Based Bagging Architecture for Sentiment Analysis,” Journal of Visual 
Languages & Computing, vol. 25, no. 6, pp. 840–849, 2014. 

[77] E. M. Alshari, A. Azman, N. Mustapha, S. C. Doraisamy, and M. Alksher, “Prediction of Rating from Comments Based on Information Retrieval and Sentiment Analysis,” 2016 Third International Conference on Information Retrieval and Knowledge Management (CAMP), pp. 
32–36, 2016. 

[78] A. Giachanou and F. Crestani, “Tracking Sentiment by Time Series 
Analysis,” Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1037– 1040, 2016. 

[79] A. Krouska, C. Troussas, and M. Virvou, “The Effect of Preprocessing Techniques on Twitter Sentiment Analysis,” 7th International Conference on Information, Intelligence, Systems Applications (IISA), pp. 1–5, 2016. 

[80] C.-L. Liu, W.-H. Hsaio, C.-H. Lee, G.-C. Lu, and E. Jou, “Movie Rating and Review Summarization in Mobile Environment,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, no. 3, pp. 397–407, 2012. 

[81] G. Qiu, B. Liu, J. Bu, and C. Chen, “Opinion Word Expansion and Target Extraction through Double Propagation,” Computational Linguistics, vol. 37, no. 1, pp. 9–27, 2011. 

[82] S. Wang, D. Li, X. Song, Y. Wei, and H. Li, “A Feature Selection Method based on Improved Fishes Discriminant Ratio for Text Sentiment Classification,” Expert Systems with Applications, vol. 38, no. 7, pp. 8696–8702, 2011. 

[83] N. Shelke,“Approaches of Emotion Detection from Text, ”International Journal of Computer Science and Information Technology Research, vol. 2, no. 2, pp. 123–128, 2014. 

[84] E. T. Brown, A. Ottley, H. Zhao, Q. Lin, R. Souvenir, A. Endert, and R. Chang, “Finding Waldo: Learning about Users from their Interactions,” IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 1663–1672, 2014.
[85] C. Liu, L. Xu, Z. Wang, and N. Wang, “Sentiment Analysis method based on Context View,” 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC- FSKD), pp. 1577–1581, 2016. 

[86] B. OF ́laherty and C. Heavin, “Positioning Predictive Analytics for Customer Retention,” Journal of Decision Systems, vol. 24, no. 1, pp. 3–18, 2015. 

[87] S. Poria, E. Cambria, G. Winterstein, and G.-B. Huang, “Sentic Patterns: Dependency-Based rules for Concept-Level Sentiment Analysis,” International Journal of Knowledge-Based Systems, vol. 69, pp. 45–63, 2014. 

[88] M. D. Munezero, C. S. Montero, E. Sutinen, and J. Pajunen, “Are they different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text,” IEEE Transactions on Affective Computing, vol. 5, no. 2, pp. 101–111, 2014. 

[89] V. Sintsova and P. Pu, “Dystemo: Distant Supervision Method for Multi-Category Emotion Recognition in Tweets,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 8, no. 1, p. 13, 2016. 

[90] C.-J. Su and Y.-A. Chen, “Social Media Analytics Based Product Improvement Framework,” International Symposium on Computer, Consumer and Control (IS3C), pp. 393–396, 2016. 

[91] X. Quan, Q. Wang, Y. Zhang, L. Si, and L. Wenyin, “Latent Discriminative Models for Social Emotion Detection with Emotional Dependency,” ACM Transactions on Information Systems (TOIS), vol. 34, no. 1, pp. 2–19, 2015.
[92] E. Kouloumpis, T. Wilson, and J. D. Moore, “Twitter Sentiment Analysis: The Good the Bad and the Omg!” Proceedings of the Fifth International Conference on Weblogs and Social Media, vol. 11, no. 538- 541, p. 164, 2011.
[93] V. Perez-Rosas, R. Mihalcea, and L.-P. Morency, “Utterance-Level Multimodal Sentiment Analysis,” Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (1), vol. 1, pp. 973–982, 2013. 

[94] J. Bollen, H. Mao, and X. Zeng, “Twitter Mood Predicts the Stock Market,” Journal of Computational Science, vol. 2, no. 1, pp. 1–8, 2011. 

[95] G. S. Solakidis, K. N. Vavliakis, and P. A. Mitkas, “Multilingual Sentiment Analysis using Emoticons and Keywords,” IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 2, pp. 102–109, 2014. 

[96] Z. Wang, S. Y. M. Lee, S. Li, and G. Zhou, “Emotion Analysis in Code-Switching Text with Joint Factor Graph Model,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 25, no. 3, pp. 469–480, 2017.

[97] Pushpa C N, Thriveni J, Venugopal K R and L M Patnaik, “Radial Basis Neural Network Technique based Web Page Recommendation System”, International Journal of Computer Sciences and Engineering (IJCSE), vol. 2, no. 9, pp. 1-7, September 2014.
[98] Y. Lu, M. Steptoe, S. Burke, H. Wang, J.-Y. Tsai, H. Davulcu, D. Montgomery, S. R. Corman, and R. Maciejewski, “Exploring Evolving Media Discourse through Event Cueing,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 220–229, 2016.
[99] S. Asur and B. A. Huberman, “Predicting the Future with Social Media,” IEEE/WIC/ACM International Conference on Web Intelligence, vol. 1, pp. 492–499, 2010.
[100] C. Clavel and Z. Callejas, “Sentiment Analysis: From Opinion Mining to Human-Agent Interaction,” IEEE Transactions on Affective Computing, vol. 7, no. 1, pp. 74–93, 2016.
[101] H. Kang, S. J. Yoo, and D. Han, “Senti-Lexicon and improved Naive Bayes algorithms for Sentiment Analysis of Restaurant reviews,” International Journal of Expert Systems with Applications, vol. 39, no. 5, pp. 6000–6010, 2012.
[102] E. Psomakelis, K. Tserpes, D. Anagnostopoulos, and T. Varvarigou, “Comparing Methods for Twitter Sentiment Analysis,” 6th Conference on Knowledge Discovery and Information Retrieval, 2015.
[103] T. H. Nguyen, K. Shirai, and J. Velcin, “Sentiment Analysis on Social Media for Stock Movement Prediction,” International Journal of Expert Systems with Applications, vol. 42, no. 24, pp. 9603–9611, 2015.
[104] S. Krening, B. Harrison, K. M. Feigh, C. L. Isbell, M. Riedl, and A. Thomaz, “Learning from Explanations using Sentiment and Advice in RL,” IEEE Transactions on Cognitive and Developmental Systems, vol. 9, no. 1, pp. 44–55, 2017.
[105] F. Li, S. Wang, S. Liu, and M. Zhang, “SUIT: A Supervised User- Item Based Topic Model for Sentiment Analysis.” Proceedings of the Twenty-Eighth Conference on Artificial Intelligence, vol. 14, pp. 1636–1642, 2014.
[106] N. Li and D. D. Wu, “Using Text Mining and Sentiment Analysis for Online Forums Hotspot Detection and Forecast,” International Journal of Decision support systems, vol. 48, no. 2, pp. 354–368, 2010.
[107] A. C. E. Lima, L. N. de Castro, and J. M. Corchado, “A Polarity Analysis Framework for Twitter Messages,” International Journal of Applied Mathematics and Computation, vol. 270, pp. 756–767, 2015.
[108] Y. Y. Chen and K. V. Lee, “User-Centered Sentiment Analysis on Customer Product Review,” International Journal of World Applied Sciences, vol. 12, pp. 32–38, 2011.
[109] Z. Yan, M. Xing, D. Zhang, B. Ma, and T. Wang, “A Context- Dependent Sentiment Analysis of Online Product Reviews based on Dependency Relationships,” Association for Information Systems, 2014.
[110] G. Li and F. Liu, “Application of a Clustering Method on Sentiment Analysis,” Journal of Information Science, vol. 38, no. 2, pp. 127–139, 2012.
[111] K. Ghag and K. Shah, “Comparative Analysis of the Techniques for Sentiment Analysis,” International Conference on Advances in Technology and Engineering (ICATE), pp. 1–7, 2013.
[112] J. Smailovic, M. Grcar, N. Lavrac, and M. Znidarsic, “Stream-based Active Learning for Sentiment Analysis in the Financial Domain,” International Journal of Information Sciences, vol. 285, pp. 181–203, 2014.
[113] W. Wei and T. Yongxin, “Network Public Sentiment Orientation Analysis based on HMM Model,” International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 2269–2273, 2016.
[114] C. Wang, Z. Xiao, Y. Liu, Y. Xu, A. Zhou, and K. Zhang, “SentiView: Sentiment Analysis and Visualization for Internet Popular Topics,” IEEE Transactions on Human-Machine Systems, vol. 43, no. 6, pp. 620–630, 2013.
[115] P. H. Calais Guerra, A. Veloso, W. Meira Jr, and V. Almeida, “From Bias to Opinion: A Transfer-Learning Approach to Real-Time Sentiment Analysis,” Proceedings of the 17th ACM SIGKDD international conference on Knowledge Discovery and Data Mining, pp. 150–158, 2011.
[116] A. Trilla and F. Alias, “Sentence-Based Sentiment Analysis for Expressive Text-to-Speech,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no. 2, pp. 223–233, 2013.
[117] P. Goncalves, M. Arau ́jo, F. Benevenuto, and M. Cha, “Comparing and Combining Sentiment Analysis Methods,” Proceedings of the first ACM Conference on Online Social Networks, pp. 27–38, 2013.
[118] X. Yu, Y. Liu, X. Huang, and A. An, “Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain,” IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 4, pp. 720–734, 2012.
[119] K. Hiroshi, N. Tetsuya, and W. Hideo, “Deeper Sentiment Analysis using Machine Translation Technology,” Proceedings of the 20th International Conference on Computational Linguistics, pp. 494–500, 2004.
[120] M. Dermouche, L. Kouas, J. Velcin, and S. Loudcher, “A Joint Model for Topic-Sentiment Modeling from Text,” Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp. 819–824, 2015.
[121] N. Claypo and S. Jaiyen, “Automatic Keyword Selection for Sentiment Analysis using Class Dependency and Dissimilarity,” 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 1–6, 2016.
[122] R. Jongeling, S. Datta, and A. Serebrenik, “Choosing your Weapons: On Sentiment Analysis Tools for Software Engineering Research,” IEEE International Conference on Software Maintenance and Evolution (ICSME), pp. 531–535, 2015.
[123] Q. Fang, C. Xu, J. Sang, M. S. Hossain, and G. Muhammad, “Word- of-Mouth Understanding: Entity-Centric Multimodal Aspect-Opinion Mining in Social Media,” IEEE Transactions on Multimedia, vol. 17, no. 12, pp. 2281–2296, 2015.
[124] H. Lee, H. J. Lee, R. A. Cruz, and J. L. Enriquez, “Electronic Word of Mouth (eWOM) in the Movie Industry,” Proceedings of the 18th Annual International Conference on Electronic Commerce: e-Commerce in Smart Connected World, pp. 1–7, 2016.
[125] E. Cambria, “An Introduction to Concept-Level Sentiment Analysis,” Mexican International Conference on Artificial Intelligence, pp. 478-483, 2013.
[126] A. G. Vural, B. B. Cambazoglu, and P. Karagoz, “Sentiment-Focused Web Crawling,” Proceedings of the 21st ACM International Conference on Information and Knowledge Management, vol. 8, no. 4, pp. 1–21, 2014.
[127] X. Li, H. Xie, L. Chen, J. Wang, and X. Deng, “News Impact on Stock Price Return via Sentiment Analysis,” International Journal