Detection of Sarcasm on Amazon Product Reviews using Machine Learning Algorithms under Sentiment Analysis

Most of the people express their ideas, views and opinions over social media. These feedbacks or comments carry an emotion in them. This data can be either straight forward or sarcastic. This text has to be analyzed and make the reviewers understand the exact intent of the writer. Sentiment analysis is used to analyze the perspective of text. Sarcasm can also be present in the text which is a bitter way of conveying the information. Selection of the dataset is the initial task. Dataset is retrieved from Amazon datasets. The next task is preprocessing of data which includes tokenization of data, polarity identification, stemming and lemmatization. Later, feature extraction is done, which includes term frequency, Inverse document frequency and n-gram. The classification algorithms are used such as Support Vector Machine (SVM), K Nearest Neighbors and Random forest are implemented. Further the calculation of results is evaluated by the parameter accuracy.