Text classification is a process where documents are categorized usually by topic, place, readability easiness, etc. For text classification by topic, a well-known method is Singular Value Decomposition. For text classification by readability, “Flesh Reading Ease index” calculates the readability easiness level of a document (e.g. easy, medium, advanced). In this paper, we propose Singular Valu...

Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learni...

Automatic handwritten script identification from document images facilitates many important applications such as sorting, transcription of multilingual documents and indexing of large collection of such images, or as a precursor to optical character recognition (OCR). In this paper, we investigate a texture as a tool for determining the script of handwritten document image, based on the observa...

Document sentiment classification is a task to classify a document according to the positive or negative polarity of its opinion (favorable or unfavorable). We propose using syntactic relations between words in sentences for document sentiment classification. Specifically, we use text mining techniques to extract frequent word sub-sequences and dependency sub-trees from sentences in a document ...

Aim of this paper is to describe a method of automatic web page classification to semantic domains and its evaluation. The classification method exploits machine learning algorithms and several morphological as well as semantical text processing tools. In contrast to general text document classification, in the web document classification there are often problems with short web pages. In this p...

The goal of sentiment classification is to detect writer’s sentiment from a document. This paper investigates which features and what combination of them is more effective in sentiment classification. Experiments show that the effective combination method of global and domain features can significantly reduce classification errors relative to features which have been used in general text classi...

In this paper we perform a comparative analysis of three models for feature representation of text documents in the context of document classification. In particular, we consider the most often used family of models bag-of-words, recently proposed continuous space models word2vec and doc2vec, and the model based on the representation of text documents as language networks. While the bag-of-word...

Now in these days the information need is increasing rapidly in our day to day life therefore a large number of users are accessing data from search engine. The search engines are composed with three major components user query interface, search algorithm and the ranking process. During search process the system evaluate the user input query and the database documents according to best fit docu...