Abbasi,, A., Hassan,, A., & Dhar,, M. (2014). Benchmarking Twitter sentiment analysis tools. In LREC (Vol. 14, pp. 26–31). Reykjavik, Iceland: European Language Resources Association (ELRA).
Abdulla,, N. A., Ahmed,, N. A., Shehab,, M. A., Al‐Ayyoub,, M., Al‐Kabi,, M. N., & Al‐rifai,, S. (2014). Towards improving the lexicon‐based approach for Arabic sentiment analysis. International Journal of Information Technology and Web Engineering, 9(3), 55–71.
Abirami,, A. M., & Gayathri,, V. (2017). A survey on sentiment analysis methods and approach. In 2016 eighth international conference on advanced computing (ICOAC), Chennai, India (pp. 72–76). Piscataway, NJ: IEEE. https://doi.org/10.1109/ICoAC.2017.7951748
Agerri,, R., & Garćıa‐Serrano,, A. (2010). Q‐wordnet: Extracting polarity from wordnet senses. In LREC. Valletta, Malta: European Language Resources Association (ELRA).
Appel,, O., Chiclana,, F., Carter,, J., & Fujita,, H. (2016). A hybrid approach to the sentiment analysis problem at the sentence level. Knowledge‐Based Systems, 108, 110–124.
Arnold,, M. (1960). Emotion and personality. New york, NY: Columbia University Press.
Baccianella,, S., Esuli,, A., & Sebastiani,, F. (2010). Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In LREC (Vol. 10, pp. 2200–2204). Valletta, Malta: European Language Resources Association (ELRA).
Balazs,, J. A., & Velásquez,, J. D. (2016). Opinion mining and information fusion: A survey. Information Fusion, 27, 95–110.
Batrinca,, B., & Treleaven,, P. C. (2015). Social media analytics: A survey of techniques, tools and platforms. AI %26 SOCIETY, 30(1), 89–116.
Boiy,, E., & Moens,, M.‐F. (2009). A machine learning approach to sentiment analysis in multilingual web texts. Information Retrieval, 12(5), 526–558.
Bouma,, G. (2009). Normalized (pointwise) mutual information in collocation extraction. In proceedings of the Biennial German Society for Computational Linguistics Conference (GSCL) (pp. 31–41).
Brody,, S., & Diakopoulos,, N. (2011). Cooooooooooooooollllllllllllll!!!!!!!!!!!!!!: using word lengthening to detect sentiment in microblogs. In Proceedings of the conference on empirical methods in natural language processing, Edinburgh, Scotland (pp. 562–570). Stroudsburg, PA: Association for Computational Linguistics.
Cambria,, E., Schuller,, B., Xia,, Y., & Havasi,, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems, 28(2), 15–21.
Carchiolo,, V., Longheu,, A., & Malgeri,, M. (2015). Using Twitter data and sentiment analysis to study diseases dynamics. In Information technology in bio‐and medical informatics (pp. 16–24). Berlin: Springer.
Chaturvedi,, I., Cambria,, E., Welsch,, R. E., & Herrera,, F. (2018). Distinguishing between facts and opinions for sentiment analysis: Survey and challenges. Information Fusion, 44, 65–77.
Chaudhari,, P., & Chandankhede,, C. (2017, March). Literature survey of sarcasm detection. In 2017 international conference on wireless communications, signal processing and networking (WiSPNET), Chennai, India (pp. 2041–2046). Piscataway, NJ: IEEE. https://doi.org/10.1109/WiSPNET.2017.8300120
Choi,, M., & Toma,, C. L. (2014). Social sharing through interpersonal media: Patterns and effects on emotional well‐being. Computers in Human Behavior, 36, 530–541.
Chopra,, F. K., & Bhatia,, R. (2016). A critical review of sentiment analysis. International Journal of Computer Applications, 149(10), 37–40.
Coppersmith,, G., Harman,, C., & Dredze,, M. (2014). Measuring post traumatic stress disorder in Twitter. In ICWSM. Palo Alto, CA: Association for the Advancement of Artificial Intelligence.
Coviello,, L., Sohn,, Y., Kramer,, A. D., Marlow,, C., Franceschetti,, M., Christakis,, N. A., & Fowler,, J. H. (2014). Detecting emotional contagion in massive social networks. PLoS One, 9(3), e90315.
da Silva,, N. F. F., Coletta,, L. F., Hruschka,, E. R., & Hruschka,, E. R., Jr. (2016). Using unsupervised information to improve semi‐supervised tweet sentiment classification. Information Sciences, 355, 348–365.
Das,, S. R., & Chen,, M. Y. (2007). Yahoo! for amazon: Sentiment extraction from small talk on the web. Management Science, 53(9), 1375–1388.
Desai,, M., & Mehta,, M. A. (2016). A hybrid classification algorithm to classify engineering students` problems and perks. arXiv preprint arXiv:1604.02358.
Devika,, M., Sunitha,, C., & Ganesh,, A. (2016). Sentiment analysis: A comparative study on different approaches. Procedia Computer Science, 87, 44–49.
Drake,, A., Ringger,, E., & Ventura,, D. (2008). Sentiment regression: Using real‐valued scores to summarize overall document sentiment. In 2008 IEEE international conference on semantic computing, Santa Clara, CA (pp. 152–157). Washington, D.C.: IEEE Computer Society.
Eichstaedt,, J. C., Schwartz,, H. A., Kern,, M. L., Park,, G., Labarthe,, D. R., Merchant,, R. M., … Seligman,, M. E. (2015). Psychological language on Twitter predicts county‐level heart disease mortality. Psychological Science, 26(2), 159–169.
Ekman,, P., & Wallace,, V. (2003). Unmasking the face. Cambridge, MA: Malor Book.
Ertugrul,, A. M., Onal,, I., & Acarturk,, C. (2017). Does the strength of sentiment matter? A regression based approach on Turkish social media. In International conference on applications of natural language to information systems (pp. 149–155).
Esuli,, A., & Sebastiani,, F. (2006). Sentiwordnet: A publicly available lexical resource for opinion mining. In Proceedings of LREC (Vol. 6, pp. 417–422). Genova, Italy: European Language Resources Association (ELRA).
Feldman,, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82–89.
Feldman,, R., & Sanger,, J. (2007). The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge, England: Cambridge University Press.
Giachanou,, A., & Crestani,, F. (2016). Like it or not: A survey of Twitter sentiment analysis methods. ACM Computing Surveys (CSUR), 49(2), 28.
Gitari,, N. D., Zuping,, Z., Damien,, H., & Long,, J. (2015). A lexicon‐based approach for hate speech detection. International Journal of Multimedia and Ubiquitous Engineering, 10(4), 215–230.
Go,, A., Bhayani,, R., & Huang,, L. (2009). Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1, 12.
Gohil,, S., Vuik,, S., & Darzi,, A. (2018). Sentiment analysis of health care tweets: Review of the methods used. JMIR Public Health and Surveillance, 4(2), e43.
Haddi,, E., Liu,, X., & Shi,, Y. (2013). The role of text pre‐processing in sentiment analysis. Procedia Computer Science, 17, 26–32.
He,, Y., & Zhou,, D. (2011). Self‐training from labeled features for sentiment analysis. Information Processing %26 Management, 47(4), 606–616.
Hidalgo,, C. R., Tan,, E., & Verlegh,, P. (2015). The social sharing of emotion (SSE) in online social networks: A case study in live journal. Computers in Human Behavior, 52, 364–372.
Hussein,, D. M. E.‐D. M. (2016). A survey on sentiment analysis challenges. Journal of King Saud University‐Engineering Sciences. Cairo, Egypt: Elsevier.
Kanayama,, H., & Nasukawa,, T. (2006). Fully automatic lexicon expansion for domain‐oriented sentiment analysis. In Proceedings of the 2006 conference on empirical methods in natural language processing, Sydney, Australia (pp. 355–363). Stroudsburg, PA: Association for Computational Linguistics.
Kim,, H.‐J., Park,, S.‐B., & Jo,, G.‐S. (2014). Affective social networkhappiness inducing social media platform. Multimedia Tools and Applications, 68(2), 355–374.
Korayem,, M., Crandall,, D., & Abdul‐Mageed,, M. (2012). Subjectivity and sentiment analysis of Arabic: A survey. In International conference on advanced machine learning technologies and applications, Cairo, Egypt (pp. 128–139). Berlin, Heidelberg: Springer.
Lalji,, T., & Deshmukh,, S. (2016). Twitter sentiment analysis using hybrid approach. International Research Journal of Engineering and Technology, 3(6), 2,887–2,890.
Li,, G., & Liu,, F. (2012). Application of a clustering method on sentiment analysis. Journal of Information Science, 38(2), 127–139.
Li,, G., & Liu,, F. (2014). Sentiment analysis based on clustering: A framework in improving accuracy and recognizing neutral opinions. Applied Intelligence, 40, 441–452.
Liu,, B. (2015). Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge, England: Cambridge University Press.
Liu,, B. (2017). Many facets of sentiment analysis. In A practical guide to sentiment analysis (pp. 11–39). Berlin: Springer.
Lo,, S. L., Cambria,, E., Chiong,, R., & Cornforth,, D. (2017). Multilingual sentiment analysis: From formal to informal and scarce resource languages. Artificial Intelligence Review, 48(4), 499–527.
Medhat,, W., Hassan,, A., & Korashy,, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113.
Mejova,, Y., & Srinivasan,, P. (2011). Exploring feature definition and selection for sentiment classifiers. In Fifth international AAAI conference on weblogs and social media multi perspective question answering (mpqa) lexicon. Barcelona, Spain: AAAI. Retrived from http://www.cs.pitt.edu/mpqa/subj lexicon.html
Mntyl,, M., Graziotin,, D., & Kuutila,, M. (2016). The evolution of sentiment analysis—A review of research topics, venues, and top cited papers (p. 27).
Nigam,, P. P., Patil,, D. D., & Patil,, Y. S. (2018). Sentiment classification of Twitter data: A review. International Research Journal of Engineering and Technology, 5(7), 929–931.
O`Connor,, B., Balasubramanyan,, R., Routledge,, B. R., & Smith,, N. A. (2010). From tweets to polls: Linking text sentiment to public opinion time series. ICWSM, 11(122–129), 1–2.
O`Dea,, B., Wan,, S., Batterham,, P. J., Calear,, A. L., Paris,, C., & Christensen,, H. (2015). Detecting suicidality on Twitter. Internet Interventions, 2(2), 183–188.
Onal,, I., Ertugrul,, A. M., & Cakici,, R. (2014). Effect of using regression on class confidence scores in sentiment analysis of Twitter data. In WASSA@ ACL (pp. 136–141).
Pak,, A., & Paroubek,, P. (n.d.). Twitter as a corpus for sentiment analysis and opinion mining (p. 10).
Palanisamy,, P., Yadav,, V., & Elchuri,, H. (2013). Serendio: Simple and practical lexicon based approach to sentiment analysis. In Proceedings of second joint conference on lexical and computational semantics (pp. 543–548).
Pang,, B., Lee,, L., & Vaithyanathan,, S. (2002). Thumbs up? sentiment classification using machine learning techniques. In EMNLP `02 proceedings of the ACL‐02 conference on empirical methods in natural language processing (pp. 79–86).
Pang,, B., & Lee,, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1–135.
Patel,, V., Prabhu,, G., & Bhowmick,, K. (2015). A survey of opinion mining and sentiment analysis. International Journal of Computer Applications, 131(1), 24–27.
Picard,, R. W. (1997). Affective computing (Vol. 252). Cambridge, MA: MIT Press.
Plutchik,, R. (1980). Theories of emotion. In R. Plutchik, & H. Kellerman, (Eds.), Emotion: Theory, research and experiences. New York, NY: Academic Press.
Pradhan,, V. M., Vala,, J., & Balani,, P. (2016). A survey on sentiment analysis algorithms for opinion mining. International Journal of Computer Applications, 133(9), 7–11.
Rajamohana,, S. P., Umamaheswari,, K., Dharani,, M., & Vedackshya,, R. (2017). A survey on online review spam detection techniques. In 2017 international conference on Innovations in green energy and healthcare technologies (IGEHT) (pp. 1–5).
Ravi,, K., & Ravi,, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge‐Based Systems, 89, 14–46.
Read,, J., & Carroll,, J. (2009). Weakly supervised techniques for domain‐independent sen‐ timent classification. In Proceedings of the 1st international cikm workshop on topic‐sentiment analysis for mass opinion, Hong Kong, China (pp. 45–52). New York, NY: ACM.
Rosenquist,, J. N., Fowler,, J. H., & Christakis,, N. A. (2011). Social network determinants of depression. Molecular Psychiatry, 16(3), 273–281.
Routray,, P., Swain,, C. K., & Mishra,, S. P. (2013). A survey on sentiment analysis. International Journal of Computer Applications, 76(10), 1–8.
Saif,, H., Fernandez,, M., He,, Y., & Alani,, H. (2013). Evaluation datasets for Twitter sentiment analysis: A survey and a new dataset, the sts‐gold. Cham, Switzerland: Springer.
Saif,, H., Fernandez,, M., He,, Y., & Alani,, H. (2014). Senticircles for contextual and conceptual semantic sentiment analysis of Twitter. In The semantic web: Trends and challenges (pp. 83–98). Berlin: Springer.
Save,, A., & Shekokar,, N. (2017). Analysis of cross domain sentiment techniques. In 2017 international conference on electrical, electronics, communication, computer, and optimization techniques (ICEECCOT) (pp. 1–9). New York, NY: IEEE. https://doi.org/10.1109/ICEECCOT.2017.8284637
Serrano‐Guerrero,, J., Olivas,, J. A., Romero,, F. P., & Herrera‐Viedma,, E. (2015). Sentiment analysis: A review and comparative analysis of web services. Information Sciences, 311, 18–38.
Sharef,, N. M., Zin,, H. M., & Nadali,, S. (2016). Overview and future opportunities of sentiment analysis approaches for big data. Journal of Computer Sciences, 12(3), 153–168.
Sharma,, K., & Kaur,, A. (2015). Personality prediction of Twitter users with logistic regression classifier learned using stochastic gradient descent. IOSR Jpurnal of Computer Engineering, 17(4), 39–47.
Singh,, N. K., Tomar,, D. S., & Sangaiah,, A. K. (2018). Sentiment analysis: A review and comparative analysis over social media. Journal of Ambient Intelligence and Humanized Computing, 1–21.
Soleymani,, M., Garcia,, D., Jou,, B., Schuller,, B., Chang,, S.‐F., & Pantic,, M. (2017). A survey of multimodal sentiment analysis. Image and Vision Computing, 65, 3–14.
Spencer,, J., & Uchyigit,, G. (2012). Sentimentor: Sentiment analysis of Twitter data. In Proceedings of European conference on machine learning and principles and practice of knowledge discovery in databases (pp. 56–66). Aachen, Germany: CEUR‐WS.org, RWTH Aachen University.
Strapparava,, C., Valitutti,, A. (2004). Wordnet affect: An affective extension of wordnet. In LREC (Vol. 4, pp. 1083–1086).
Taboada,, M., Anthony,, C., & Voll,, K. (2006). Methods for creating semantic orientation dictionaries. In Proceedings of the 5th conference on language resources and evaluation (LREC06) (pp. 427–432). Cambridge, MA: MIT Press.
Taboada,, M., Brooke,, J., Tofiloski,, M., Voll,, K., & Stede,, M. (2011). Lexicon‐based methods for sentiment analysis. Computational Linguistics, 37(2), 267–307.
Tang,, D., Qin,, B., & Liu,, T. (2015). Deep‐learning for sentiment analysis: Successful approaches and future challenges. WIREs: Data Mining and Knowledge Discovery, 5(6), 292–303.
Thakkar,, H., & Patel,, D. (2015). Approaches for sentiment analysis on Twitter: A state‐of‐art study. arXiv preprint arXiv:1512.01043.
Thelwall,, M., Buckley,, K., Paltoglou,, G., Cai,, D., & Kappas,, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544–2558.
Turney,, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting on association for computational linguistics (pp. 417–424). New York, NY: ACM.
Turney,, P. D., & Littman,, M. L. (2003). Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS), 21(4), 315–346.
Vivek,, N., Ishan,, A., & Arjun,, B. (2013). Fast and accurate sentiment classification using an enhanced Naive Bayes model. CoRR, abs/1305.6143.
Wiebe,, J., Wilson,, T., Bruce,, R., Bell,, M., & Martin,, M. (2004). Learning subjective language. Computational Linguistics, 30(3), 277–308.
Wilson,, T. (2005). Opinionfinder: A system for subjectivity analysis. In Hlt‐demo `05 proceedings of hlt/emnlp on interactive demonstrations (pp. 34–35). Stroudsburg, PA: Association for Computational Linguistics.
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). Stroudsburg, PA: Association for Computational Linguistics.
Yadollahi,, A., Shahraki,, A. G., & Zaiane,, O. R. (2017). Current state of text sentiment analysis from opinion to emotion mining. ACM Computing Surveys (CSUR), 50(2), 25.
Yang,, C.‐S., & Shih,, H.‐P. (2012). A rule‐based approach for effective sentiment analysis. In PACIS (p. 181). Atlanta, Georgia: Association for Information Systems.
Zhang,, L., Wang,, S., & Liu,, B. (2018). Deep learning for sentiment analysis: A survey. WIREs Data Mining and Knowledge Discovery, 8(4), e1253.
Žǐzka,, J. (2015). Modern computational models of semantic discovery in natural language. Hershey, PA: IGI Global.