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Automatic classification of blog entries is generally treated as a semi-supervised machine learning task, in which the blog entries are automatically assigned to one of a set of pre-defined classes based on the features extracted from their textual content. 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. Empirical evaluations indicate that this multi-step classification approach has resulted in good overall classification accuracy over unstructured blog text datasets with both machine learning model alternatives. However, the na?ve Bayesian classification model clearly out-performs the ANN based classification model when a smaller feature-set is available which is usually the case when a blog topic is recent and the number of training datasets available is restricted.

Automatic Text Classification is a semi-supervised machine learning task that automatically assigns a given document to a set of pre-defined categories based on its textual content and extracted features. Automatic Text Classification has important applications in content management, contextual search, opinion mining, product review analysis, spam filtering and text sentiment mining. This paper explains the generic strategy for automatic text classification and surveys existing solutions to major issues such as dealing with unstructured text, handling large number of attributes and selecting a machine learning technique appropriate to the text-classification application.

The growth of E-commerce has led to the invention of several websites that market and sell products as well as allow users to post reviews. It is typical for an online buyer to refer to these reviews before making a buying decision. Hence, automatic summarization of users’ reviews has a great commercial significance. However, since the product reviews are written by nonexperts in an unstructured, natural language text, the task of summarizing them is challenging. This paper presents 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. It includes various phases like preprocessing and feature extraction and pruning followed by feature-based opinion summarization and overall opinion sentiment classification. Empirical studies indicate that the approach used in the paper can identify opinionated sentences from blog reviews with a high average precision of 91% and can classify the polarity of the reviews with a good average accuracy of 86%.

Automatic Text Classification is a semi-supervised machine learning task that automatically assigns a given text document to a set of pre-defined categories based on the features extracted from its textual content. 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 naive Bayesian classification. Empirical evaluation of this technique has resulted in a classification accuracy of approximately 87% over the test set. In addition to classifying the textual entries of sports blogs, it is proposed that the extracted features themselves be further classified under more meaningful heads which results in generation of a semantic resource that lends greater understanding to the classification task. This semantic resource can be used for data mining requirements that arise in the future.

Automatic Text Summarization is a specialized text mining task of generating a summary or abstract from single or multiple input text documents. Various heuristic and semi-supervised learning methods have been explored by researchers in this field to generate generic as well as user-oriented summaries. This paper examines the effectiveness of well-known summarization heuristics when applied to the task of generating single-document summary extracts of variable length. For evaluating the quality of the summaries, the original text documents and their summaries were scored by different human judges based on soft metrics like topic-coverage, relative coherence, novelty and information content; and their scores were statistically compared. It was experimentally verified that in 65% of the documents there was less than 10% variance between the scores assigned to the original texts and their summaries.