The aim of this study is to provide an overview the state-of-the-art elements of text
classification. For this purpose, we first select and investigate the primary and recent studies …

Sentiment analysis is a solution that enables the extraction of a summarized opinion or
minute sentimental details regarding any topic or context from a voluminous source of data …

Massive textual data management and mining usually rely on automatic text classification
technology. Term weighting is a basic problem in text classification and directly affects the …

Purpose Rapid developments in social networks and their usage in everyday life have
caused an explosion in the amount of short electronic documents. Thus, the need to classify …

Term weighting is a strategy that assigns weights to terms to improve the performance of
sentiment analysis and other text mining tasks. In this paper, we propose a supervised term …

Identifying product features from reviews is the fundamental step as well as a bottleneck in
feature-level sentiment analysis. This study proposes a method of unsupervised product …

Term weighting is a well-known preprocessing step in text classification that assigns
appropriate weights to each term in all documents to enhance the performance of text …

The accuracy of a text classification method based on a SVM learner depends on the
weighting metric used in order to assign a weight to a term. Weighting metrics can be …

The effective representation of the relationship between the documents and their contents is
crucial to increase classification performance of text documents in the text classification …

Text classification is one of the popular high dimensional classification problems where
providing better feature vector representations explicitly improve classification …