Abstract: A computer-implemented method for product search using the User-Weighted, Attribute-Based, Sort-Ordering comprising the steps of: computing of specification score for product attribute; computing of sentiment score for product attribute; characterized by steps of: —extracting reviews for each product from multiple sources; detecting the attributes described in each product review; detecting the polarity (positive/negative) of the user review with respect to each attribute converting the said attributes into a numerical score for each attribute which captures all the information about that attribute from user-ratings; computing an overall product score using the specifications score and sentiment score for individual product attributes; and displaying the search results sorted according to the overall product score.

Abstract: A method for text classification and feature selection using class vectors, comprising the steps of receiving a text/training corpus including a plurality of training features representing a plurality of objects from a plurality of classes; learning a vector representation for each of the classes along with word vectors in the same embedding space; training the class vectors and words vectors jointly using skip-gram approach; and performing class vector based scoring for a particular feature; and performing feature selection based on class vectors.