This review presents a scope of the recent advances in this area. IEEE Trans Multimed 18(9):1910---1921, Li N, Zhai S, Zhang Z, Liu B (2017) Structural correspondence learning for cross-lingual sentiment classification with one-to-many mappings. In: Proceedings of the 2008 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, vol 01, 2008. (task-oriented, granularity-oriented, methodology-oriented) in the area of sentiment analysis. In: International conference on applications of natural language to information systems, 2017. Furthermore, in many application scenarios, it is often interesting, and in some cases critical, to discover patterns and trends based on geographical and/or temporal partitions, and keep track of how they will change overtime. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, 2017. In: Tang D, Wei F, Yang N, Zhou M, Liu T, Qin B (2014) Learning sentiment-specific word embedding for Twitter sentiment classification. But if it’s a storm of negative posts, it might not be so great after all. In: International workshop on multiple classifier systems, 2013. The discipline that deals with automatic classification techniques of emotional categories from texts into documents is Sentiment Analysis (SA); it includes linguistic and semantic analysis techniques, text mining and statistical and machine learning approaches that operate on a corpus of documents on which terms related to emotional categories are annotated and elaborated. Detection of fintech P2P lending issues in Indonesia, GIS-based fuzzy sentiment analysis framework to classify urban elements according to the orientations of citizens and tourists expressed in social networks, Exercise? 2003 IEEE computer society conference on computer vision and pattern recognition, 2003. search the data space efficiently and effectively. Neural Netw 63:104---116, Poria S, Chaturvedi I, Cambria E, Hussain A (2016) Convolutional MKL based multimodal emotion recognition and sentiment analysis. Existing surveys of sentiment analysis studies either concentrate on enumerating technical details in the areas of social media analysis, text mining, natural language processing, and dataminingorjustfocusonacertainaspectsofsentimentanalysisresearches.Moreover,the rapiddevelopmentofthefield,tosomeextent,makesthosesurveysoutdated.Besides,thereis … Social analysis frequently involves issues of equality and social justice, but the insight gained from combining social analysis techniques and CRM analytics can also help organizations create business strategies and policies that are sensitive to particular social issues and likely to be perceived by customers as having a positive social impact. With the rapid growth of web technology and easy access of internet, online shopping has been increased. Springer, pp 133---144, Qiu G, Liu B, Bu J, Chen C (2011) Opinion word expansion and target extraction through double propagation. We will begin by reviewing the literature on measurement of state capacity. Today's Internet is awash in memes as they are humorous, satirical, or ironic which make people laugh. The key novel Sentiments or opinions from social media provide the most up-to-date and inclusive infor- mation, due to the proliferation of social media and the low barrier for posting the message. Image Vis Comput 31(2):153---163, Wöllmer M, Weninger F, Knaup T, Schuller B, Sun C, Sagae K, Morency LP (2013) Youtube movie reviews: sentiment analysis in an audio-visual context. In: 2016 IEEE 16th international conference on data mining (ICDM), 2016. Association for Computational Linguistics, pp 581---586, Guo H, Zhu H, Guo Z, Zhang X, Su Z (2010) OpinionIt: a text mining system for cross-lingual opinion analysis. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. IEEE, pp 108---117, Cha M, Pérez J, Haddadi H (2009) Flash floods and ripples: The spread of media content through the blogosphere. data are made available online. NLP's favored applications, such as translation systems, search engines, natural language assistants, sentiment, and opinion analysis, are resolving societal issues at an unprecedented rate, Recently, a deeper level of data exploration has Findings