Mita K. Dalal

TL;DR: A semisupervised approach for mining online user reviews to generate comparative feature-based statistical summaries that can guide a user in making an online purchase is presented.

TL;DR: This paper attempts automatic classification of unstructured blog entries by following pre-processing steps like tokenization, stop-word elimination and stemming; statistical techniques for feature set extraction, and feature set enhancement using semantic resources followed by modeling using two alternative machine learning models—the na?ve Bayesian model and the artificial neural network model.

TL;DR: This paper attempts to automatically classify the textual entries made by bloggers on various sports blogs, to the appropriate category of sport by following steps like pre-processing, feature extraction and naïve Bayesian classification.

TL;DR: This paper examines the effectiveness of well-known summarization heuristics when applied to the task of generating single-document summary extracts of variable length and finds that in 65% of the documents there was less than 10% variance.