Social media analytics: Blog analytics for finding vulnerability
Material type:
TextDescription: MSC DA 2017-2019Subject(s): Dissertation note: MSC DA 2017-2019 INT
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | |
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Project Reports
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Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre | Non Fiction | Not for loan | R-1547 |
Sentiment analysis in natural language processing (NLP) is an important task
where the text content have several opinions about events, product and movie
reviews, trading, marketing, etc. It is an automated process of understanding an
opinion about a given subject from written or spoken language. In recent studies,
researchers have performed the hand-crafted features in sentiment analysis and
machine learning methods such as support vector machines, naive Bayes, conditional
random field, maximum entropy method, etc. but sentiment analysis will
be very difficult for agglutinative language like Malayalam. The proposed work
deals with the categorization of blogs according to its vulnerability, and specify
them as Very Good, Good, Less Vulnerable, Vulnerable, Highly vulnerable.
Keywords : Support Vector Machine, Malayalam, Sentiment Analysis, Natural
Language Processing
MSC DA 2017-2019 INT Dr Manoj Kumar T K
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