LIRNEasia is a regional ICT policy and regulation think tank active across the Asia Pacific

Indian media discuss LIRNEasia solution to TRAI’s information overload problem

Just a few days ago, the big data team posted some thoughts on how TRAI could analyze the one million plus comments it received in response to its consultation paper on OTT services. The Business Standard has extensively quoted from that collectively authored suggestions on how technology could help productively mobilize the flood of citizen ideas enabled by technology.

Last year, the corporate affairs ministry had commissioned a platform to receive responses on the hundreds of sections and sub-sections of the Companies Act. The platform, built by Corporate Professionals, allowed section-wise responses; it classified responses under different heads such as drafting errors and conceptual issues. Further, separate log-in ids were provided for different sections of stakeholders. This organised collection of responses helped the ministry compile the data in a matter of two-three days, says Pavan Kumar Vijay, managing director, Corporate Professionals.

While establishing a dedicated platform to receive comments with stock responses can be a solution, big data experts suggest using natural language processing (NLP) tools and opinion mining techniques.

Recently, some big data experts at LIRNEasia, a Colombo-based information and communication technology think-tank, brainstormed on the issue. A paper posted by Sriganesh Lokanathan, team leader (big data research, LIRNEasia, discusses possible solutions, including NLP tools such as word clouds and semantic analysis. “Individuals or groups responding to questions might have specific interests or motives in taking part in the debate. They might use words/phrases relevant to such interests or motivations consistently across questions, though not necessarily frequently enough to be noticed in the word clouds for each question. A word cloud that visualises the aggregate of all responses to all questions for which words/phrases have been excluded might serve to highlight underlying patterns of interest of the respondents,” the paper said.

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