Sentiment Mining

Cloud-based Sentiment Analysis Platform to analyze both mainstream and social media coverage of key issues.

01

Business Problem

A leading university wanted to analyze both mainstream and social media coverage of key political issues . The analysis needed to include the evaluation and mapping of the media landscape from several perspectives and based on large-scale data collection of media stories published on the web and shared on Twitter.

02

Solution

Cloud-based Sentiment Analysis Platform

  • Help users to perform detailed multilingual sentiment analysis of texts from different sources – News RSS Feed and social Media sources.
  • Used NLU / P, machine learning algorithms to extract, identify, & characterize the sentiment content
  • Language detection and Parsing performed through Sentiment Analysis Engine & Machine Learning
  • Majority of AWS NLP component rebuilt

03

Business ROI

  • Time-Saving : Using Predikly’s NLP tool the report was produced in 4-6 weeks, where in it used to take more than 8 months.
  • Increased Efficiency : Instead of a team of 15 people , the report could be built using a team of 4 people.
  • $s Saving : Resulting in saving of $150 K for each report , university if planning to release 6 such reports this year, resulting $900K in saving every year.
  • Increased Throughput : Ability to produce more reports in a shorter amount of time.

04

Tools and Technologies

  • Python

  • PostgreSQL

  • Mongo DB

  • AMCharts

  • Python-Flask

  • AngularJs

  • HTML

  • CSS

Analysed Performance

4,000+

Tweets

2,000+

News

8,000+

RSS Feeds

75%-95%

Accuracy

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"Very dedicated team and highly motivated to understand the business proposition behind the project. Sticks to deadlines and very positive people inside the team. Pleasure to work with."

. . .

CEO

Joinville AB . Sweden

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