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This model scores the sentiment of text with a value between 0 ("negative") , 0.5 ("neutral") up to 1 ("positive"). This model was trained on reviews. These were truncated to a maximum of 200 words and only the 20,000 most common words in the reviews are used.
In this project we have developed ML algorithm for opinion & suggestion mining. We have designed our model for customers & organizations. Customers can receive the percentage of positive & negative reviews. Similarly, organizations will receive the percentage of positive and negative reviews, along with improvement tips for their product/service.
Streamline and automate the process of consolidating multiple evaluations by different Hiring managers, Technical Leads, Team Leads in the candidate hiring process and generate an analysis of the all the evaluations to make hiring decisions easily and effectively.