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This paper describes the approach we take to social media analysis, combining the exploration of the opinion of text and centered on the recognition of entities and events. We examine a particular use case, which is to help archivists select materials for inclusion in a social media archive to preserve community memories, moving towards structured preservation around semantic categories. The textual approach we adopt is rule based and relies on a number of sub-components, taking into account issues inherent in social media such as noisy non-grammatical text, use of insulting words, short language popularly called as SLANG, and so on. In order to resolve the ambiguity and provide additional contextual information, we propose two major innovations in this work: first, the novel combination of tools for extracting texts and multimedia opinions; and second, the adaptation of NLP tools for the analysis of opinion specific to the problems of social media.
Keywords:
sentiment analysis, slangs, combinatorial and categorical grammar, tokenization.
Cite Article:
"Review paper on sentiment analysis on social networking sites ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.2, Issue 2, page no.4 - 8, February-2017, Available :http://www.ijrti.org/papers/IJRTI1702002.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator