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2013pp. 539–542

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Erik Cambria, Dheeraj Rajagopal, Daniel Olsher, Dipankar Das

Abstract

As the Web rapidly evolves, Web users too are evolving with it. In an era of social connectedness, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, Wikis, and other online collaborative media. In recent years, this collective intelligence has spread to many different areas, with particular focus on fields related to everyday life such as commerce, tourism, education, and health, causing the size of the social Web to expand exponentially. The distillation of knowledge from such a large amount of unstructured information, however, is an extremely difficult task, as the contents of today's Web are perfectly suitable for human consumption, but remain hardly accessible to machines. Big social data analysis grows out of this need and it includes disciplines such as social network analysis, multimedia management, social media analytics, trend discovery, and opinion mining. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences, in particular, has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction. This has led to the emerging fields of opinion mining and sentiment analysis, which deal with information retrieval and knowledge discovery from text using data mining and natural language processing (NLP) techniques to distill knowledge and opinions from the huge amount of information onCONTENTSFrom Small to Big Social Data Analysis ........................................................... 401 Large-Scale Sentiment Analysis and Tracking ................................................403 Toward the Concept-Level Analysis of Big Sentiment Data .........................406 Conclusion ........................................................................................................... 412 References ............................................................................................................. 412the World Wide Web. Opinion mining and sentiment analysis are branches of the broad field of text data mining [21] and refer generally to the process of extracting interesting and nontrivial patterns or knowledge from unstructured text documents. They can be viewed as an extension of data mining or knowledge discovery from (structured) databases [12,36]. As the most natural form of storing information is text, opinion mining is believed to have a commercial potential higher than that of data mining. Opinion mining, however, is also a much more complex task, as it involves dealing with text data that are inherently big, unstructured, and fuzzy.

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Abstract

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