Role of Machine Learning in Fake Review Detection

In today's culture the growing technology is promoting a lot of products and events in a very positive way. Technology usage in current generation has taken a new step in reaching great heights. But when a technology brings in so much positiveness it also has its own negative usage and one among them is the fake reviews. Fake reviews are weakening the actual worth of the product. To be more specific, the reviews can be divided into two categories: legitimate fake reviews and reviews written intentionally to decapitate the product or brand value. On the other hand, the machine learning algorithms are extensively used. The incorporation of machine learning techniques into the classification of the reviews is considered as an excellent combination. In this work, various datasets from different industries such as airline industry, movie industry and food industry are considered and fake reviews are classified using various algorithms including K-Nearest Neighbors, Naive Bayes, Random Forest, Decision tree, Support Vector Machine, Logistic Regression from Machine learning. There are reviews which can be decoded using the sentiment analysis from Natural Language Programming. Sentiment analysis is used to find the emotion in a text. The accuracy parameter result is analyzed for all the implemented models. The results demonstrate support vector machine technique giving high accuracy compared to other machine learning classification techniques.