Multi-label Sentiment Analysis of Product Reviews of Online Shop

Online shopping has become very popular nowadays. It tends to share user experiences of buying products or dealing with the seller by posting reviews. So millions of reviews are being generated daily. It can be difficult for a new customer of that particular product to read all of those reviews and decide whether or not to purchase. In this situation, an overall sentiment(s) of the whole review might help them. Also, people being more creative sometimes post-sarcastic or ironic statements. This may mislead other buyers. The majority of the previous research work regarding product sentiment analysis was confined to two to three sentiments only. Also, a review may express several emotions at once. By doing binary classification, we may miss other emotions present alongside the predicted one. So we have proposed a binary classifier model to separate the sarcastic reviews and then a multi-label classifier model to detect the emotions present in a particular review. We have applied several methods for multi-label classification naming binary relevance, classifier chain, and label powerset on up to four different classifiers. Among them, the OnevsRest classifier along with the support vector classifier as an estimator performed better than the other methods. We have also trained and tested a few binary classifiers for sarcasm detection and got almost the same accuracy of 93.37 and 93.92% for logistic regression and support vector classifier.