Social Media Analysis based on Semanticity of Streaming and Batch Data
This work addresses the need to process streaming and batch data from social media for applications in cognitive science, but it appears incremental as it builds on existing research without specifying broad advancements.
The paper tackles the problem of extracting semantic information from social media micro-posts for Named Entity Recognition and Author Profiling, using Conditional Random Fields for entity recognition and proposing a novel approach to identify author sociolect aspects like gender and age group.
Languages shared by people differ in different regions based on their accents, pronunciation and word usages. In this era sharing of language takes place mainly through social media and blogs. Every second swing of such a micro posts exist which induces the need of processing those micro posts, in-order to extract knowledge out of it. Knowledge extraction differs with respect to the application in which the research on cognitive science fed the necessities for the same. This work further moves forward such a research by extracting semantic information of streaming and batch data in applications like Named Entity Recognition and Author Profiling. In the case of Named Entity Recognition context of a single micro post has been utilized and context that lies in the pool of micro posts were utilized to identify the sociolect aspects of the author of those micro posts. In this work Conditional Random Field has been utilized to do the entity recognition and a novel approach has been proposed to find the sociolect aspects of the author (Gender, Age group).