Abstract
Text mining and sentiment analysis have received huge attention recently, specially because of the availability of vast data in form of text available on social media, e-commerce websites, blogs and other similar sources. This data is usually unstructured and contains noise, therefore the task of gaining information is complex and expensive. There is a growing need for developing different methodologies and models for efficiently processing the texts and extracting apt information. One way to extract information is text mining and sentiment analysis, that include: data acquisition, data pre-processing and normalization, feature extraction and representation, labelling, and finally the application of various Natural Language Processing (NLP) and machine learning algorithms. This paper provides an overview of different methods used in text mining and sentiment analysis elaborating on all subtasks.
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