Abstract
Sentiment analysis, which addresses the computational treatment of opinion, sentiment, and subjectivity in text, has received considerable attention in recent years. In contrast to the traditional coarse-grained sentiment analysis tasks, such as document-level sentiment classification, we are interested in the fine-grained aspect-based sentiment analysis that aims to identify aspects that users comment on and these aspects’ polarities. Aspect-based sentiment analysis relies heavily on syntactic features. However, the reviews that this task focuses on are natural and spontaneous, thus posing a challenge to syntactic parsers. In this paper, a set of words are defined in a dictionary. In any chatting application or in social media, the user will have many followers and there will be lots of messages or tweets. If the member of the group uses the offense word will be blocked by the page administrator, thus providing a comfort level to the other users from such harmful users. This will help to maintain a social networks for its dedicated purpose.
Keywords
Cybersecurity
Sentiment Classification
Text Analytics
Deep Learning
Social Network Analysis
Authors
How to Cite this Article
Banu.M, Muthra.S, Hema.S, Nalini.S (2016).
"SECURED SOCIAL MEDIA USING ASPECT BASED SENTIMENT ANALYSIS".
International Journal of Contemporary Research in Computer Science and Technology,
2(3), pp. 591-595.
