{'fraction_non_alphanumeric': 0.011746280344557557, 'fraction_numerical': 0.0, 'mean_word_length': 4.8090909090909095, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Recommendation System Based on Text Analysis

Recommendation Systems have gathered a lot of attention from the research community following the introduction of internet. Internet provided platform for development and deployment of web, mobile and desktop-based applications. The overall penetration of the internet has increased across the globe over the last two decades which in turn provides more customers for the tech companies. In this project, we are mostly focussing on E-commerce companies like Amazon. It is never easy to find a product that has all the features you need. We have developed a model that can be used to assist the customers in choosing the best product available that has features as specified by the customer. This model will list down all the products that have that feature. Additionally, it will also provide a feedback to the manufacturer of the product regarding the features that did not impress most of the customers. The manufacturer can work on these features and improve them when launching upgraded versions or new products in the same category. The core idea of this project is to analyse the product reviews given by existing customer to assist a new customer in choosing the best product having the feature as specified by the customer.