Fake review detection : framework‌, challenges and future By Vinodhini Ranganathan [MLDS2020]

Machine Learning Developers Summit 2020For more details, visit: https://www.mlds.analyticsindiasummit...

With the boom of the digital online platforms and social mediums, reviews posted by customers is making a huge impact on a buy or no buy decision. According to a recent survey, 85% of users read reviews of products , and 68% of them say they rely on reviews when making purchasing decisions. An incorrect or misleading review can turn out to be disastrous for customers who have fallen prey to fake positive reviews or conversely for the businesses losing potential customers basis spurious negative reviews posted by competitors or some unscrupulous agents. Sentiment Analysis (SA) / Opinion Mining(OM) has become one of the most essential components of text analytics due to its promising commercial benefits. One of the main issues in OM apart from extracting emotions and polarity is to detect fake positive reviews and negative reviews. Amazon says that out of the 1.8 million unverified reviews posted in March 2019, 99.6% were five-star. By comparison, during 2017-2018, the number of unverified reviews averaged fewer than 300,000 per month and only 75% were five-star. This talk will focus on 1. The ever-increasing problem of fake reviews and challenges in identifying them. 2. Annotating fake reviews 3. General framework for identifying fake reviews. 4. NLP techniques that can be leveraged for identifying fake reviews.


Vinodhini is currently a Senior data scientist @Cisco. She has 10+ years of work experience across Insurance, e-commerce and healthcare domains . She has worked across technologies ranging from Mainframes to Machine Learning. She is an extensive NLP researcher and one of her case studies on text and sentiment analysis has been published at Harvard Business Review. She is a passionate teacher and is currently a guest faculty for social media analytics NLP at IIMB , IIML and IMT to name a few. She is adept in text mining , NLP ,Machine learning and social media analytics. Prior to Cisco , she was an entrepreneur and has also worked across organisations like Cognizant, TCS and Sopra Steria.