Abstract
Internet has evolved to the Web 2.0 allowing people all around the world to interact with each other and to speak freely about any relevant topic. This kind of user-generated content represents an unstructured knowledge source of undeniable interest in decision-making for both common people and organizations. However, given the high volume of data stored in the Web, performing a manual analysis of this information becomes (practically) impossible. In such a context, Sentiment Analysis aims to automatically summarize opinions expressed in texts providing understandable sentiment reports. However, the computational analysis of opinions is inevitably affected by inherent difficulties presented in natural language. Ambiguity, anaphora, and ellipsis, are examples of context-dependant problems attached to natural language. In this paper, we present a lexicon-based algorithm dealing with sentiment analysis that takes advantage of context analysis to provide sentiment summarization reports.
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Moreo, A., Castro, J.L., Zurita, J.M. (2012). Handling Context in Lexicon-Based Sentiment Analysis. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 298. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31715-6_27
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DOI: https://doi.org/10.1007/978-3-642-31715-6_27
Publisher Name: Springer, Berlin, Heidelberg
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