Sentiment analysis is the process of identifying the subjective information in the source materials towards an entity. It is a subfield of text and web mining. Web is a rich and progressively expanding source of information. Sentiment analysis can be modelled as a text classification problem. Text classification suffers from the high dimensional feature space and feature sparsity problems. The ...

Background. The last few years have seen much success of applying deep networks in many important applications in Natural Language Processing: sentiment analysis, document classification, machine translation, conversational/dialogue modeling, automatic Q&A. An important trait of all of these models is that they read all the text available to them. While it is essential for certain applications,...

The sentiment detection of texts has been witnessed a booming interest in recent years, due to the increased availability of online reviews in digital form and the ensuing need to organize them. Till to now, there are mainly four different problems predominating in this research community, namely, subjectivity classification, word sentiment classification, document sentiment classification and ...

Text mining is an emerging technology that can be used to augment existing data in corporate databases by making unstructured text data available for analysis. The incredible increase in online documents, which has been mostly due to the expanding internet, has renewed the interest in automated document classification and data mining. The demand for text classification to aid the analysis and m...

We present ExB Medical Text Miner – a text mining pipeline for processing biomedical documents. This application employs stateof-the-art Named Entity Recognition, using linguistic features and word embeddings in a fully-connected second-order Conditional Random Field model, as well as a novel two-stage Relation Extraction module that first detects entity-level relations using a Support Vector C...

Transforming of text documents to real vectors is an essential step for text mining tasks such as classification, clustering and information retrieval. The extracted vectors serve as inputs for data mining models. Large vocabularies of natural languages imply a high dimensionality of input vectors; hence a substantial dimensionality reduction has to be made. We propose a new approach to a vecto...

Text mining area enables people to extract the relevant information from the given text. This approach is advantageous, because it allows the users to retrieve the useful information and helps to avoid unambiguity in the text. Text Mining tasks are classified as text classification, text clustering and summarization of documents. Phrases are important in the field of text mining and information...

Text document categorization involves large amount of data or features. The high dimensionality of features is a troublesome and can affect the performance of the classification. Therefore, feature selection is strongly considered as one of the crucial part in text document categorization. Selecting the best features to represent documents can reduce the dimensionality of feature space hence in...

Linear text classification algorithms work by computing an inner product between a test document vector and a parameter vector. In many such algorithms, including naive Bayes and most TFIDF variants, the parameters are determined by some simple, closed-form, function of training set statistics; we call this mapping mapping from statistics to parameters, the parameter function. Much research in ...

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...