Interpretation and Classification of Emotions on Computational Social Science
Gayathry Sobhanan Warrier, M Arshey, Jency Rena N M · Zenodo (CERN European Organization for Nuclear Research) · 2021
Interpretation and Classification of Emotions is per- haps one of the most popular applications of NLP. Categorization and elucidation of emotions is contextual text mining that is related with the analysis and understanding of human emotions from textual data. In the area of tourism, the internet plays a major role in the advertisement of hotels. Travelers convey their experience in the hotel by posting reviews or comments on the internet. The Vendors can be benefited by considering and correcting user reviews on the internet to improvise and assess their hotels. With the reviews available in abundance over social media , excursionists find it difficult to understand all the reviews whether they have positive or negative suggestions. It takes Computational Social Science to rapidly identify if the review is a positive or negative review. Evolution in social platforms such as tourist blogs, Twitter, Facebook and LinkedIn has fueled interest in Interpretation and Classification of Emotion. This paper focuses on extracting different categories of customer's reviews about various places of stay and analyzing the category that gives better results. Multilayer Perceptron model is used for classification of data into negative and positive sentiment categories. Classifying the data into positive and negative classes with fewer mis-classifications is the primary focus.