Description

In the recent years, the categorization of text documents into predefined classifications has perceived a growing interest due to the growing of documents in digital form and needs to organize them. Text categorization is one of the extensively used for natural language processing (NLP) applications have achieved using machine learning algorithms. Text classification is a challenging researcher to find the best suitable structure and technique. Classification process done using manual and automatic classification. This research paper covers the pre-processing, feature extraction, different algorithms and techniques for text classification and finally evaluates the performance metrics for assessment. Text pre-processing is a common task in machine learning applications that involves handlabeling sets. Although automatic and semi-automatic annotation of text data is a growing field, researchers need to develop models that use resources as efficiently as possible for a learning task. To achieve this goal, we evaluated the performance of one common pretrained machine learning models (Decision Tree) and compared their accuracy levels for DT with Amazon review and DT with Spam Detection. Finally, the system can estimate some performance metrics such as accuracy, precision, recall and f1-score for both algorithms and compare the algorithms based on accuracy in the form of graph. These results are promising, as they show that machine learning techniques could be used for the sentiment analysis for amazon review and detect the spam messages.