Collaborating Authors


Big Data in Customer Sentiment Analysis


Big data enables businesses to thrive and grow by finding hidden patterns in data. Brands are now getting smarter by taking actions based on customer sentiments. Not only brands but also political parties and governments are looking at social sentiments as a valuable resource for growth. With big data, real-time customer sentiment analysis has become possible. Social media has completely changed how people express themselves.

Extracting Actionable Knowledge from Domestic Violence Discourses on Social Media Machine Learning

Domestic Violence (DV) is considered as big social issue and there exists a strong relationship between DV and health impacts of the public. Existing research studies have focused on social media to track and analyse real world events like emerging trends, natural disasters, user sentiment analysis, political opinions, and health care. However there is less attention given on social welfare issues like DV and its impact on public health. Recently, the victims of DV turned to social media platforms to express their feelings in the form of posts and seek the social and emotional support, for sympathetic encouragement, to show compassion and empathy among public. But, it is difficult to mine the actionable knowledge from large conversational datasets from social media due to the characteristics of high dimensions, short, noisy, huge volume, high velocity, and so on. Hence, this paper will propose a novel framework to model and discover the various themes related to DV from the public domain. The proposed framework would possibly provide unprecedentedly valuable information to the public health researchers, national family health organizations, government and public with data enrichment and consolidation to improve the social welfare of the community. Thus provides actionable knowledge by monitoring and analysing continuous and rich user generated content.