Paper Details

Natural Language Processing (NLP) and Machine Learning concepts are acclaimed in today’s digitalizationof data. Over the time, value of the data keeps changing and it is important to tackle that value for performing in depthresearch in various domains. Over the past decade, natural language processing has gained much importance because itreveals a lot of unseen information in the texts. It is difficult to discover the information of interest from a huge volume ofthe text data. Thus, information extraction based on computational text processing is necessary. For many of informationmanagement goals, the task of recognising phrases and words in free text which falls under particular classes of interestis an important first step. It is crucial to manage huge amount of text being generated dramatically. The text can be forexample clinical and biomedical text. Features can be extracted for classification of the documents. Feature extraction isextracting an important subset of features from a data for improving the classification task. Correctly identifying therelated features in a text is important. Therefore, applying and expanding NLP techniques can help to better understandand study the data. This paper aims at analysing the clinical literature for cancer. The feature extraction methods suchas bag of words, tf-idf, word2vec are compared for clinical text analysis. The extracted features are evaluated againstLogistic Regression and Random Forest Classifier.

Resham N. Waykole, Anuradha D. Thakare, “A REVIEW OF FEATURE EXTRACTION METHODS FOR TEXT CLASSIFICATION”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 5, Issue 4, pp. 351-354, April 2018.