CoMoSAOVA: Computational model for sentiment analysis and online visibility assessment

Abstract

The online visibility of a brand, product or organization could influence customers’ decision to patronize it. Online reviews are opinions or emotions of customers that reveal their perception about the product over a period of time. The manual identification of features and sentiments toward an entity is a difficult task. In this study, a computational model was proposed to measure online visibility by mining Twitter data on discourse about a corporate entity – University of The Gambia. The numbers of Twitter posts, followers, followings, likes, retweets, quotes, replies and mentions serves as metrics for online visibility assessment. A linear regression evaluation between the age of the entity’s account and the number of its followers showed 96.81% correlation. This shows that the older an active Twitter account, the higher the  chance of increasing its  followers and its visibility. Another section of the model predicts the tweet sentiment of the  entity’s followers with an  accuracy of 93.68% using support vector machine and multilayer perceptron neural network.  The computed average  sentiment score of the case study was 0.3531996 based on Valence Aware Dictionary of  sEntiment Reasoning (VADER)  model. This means that positive sentiments were expressed in discussions on various  issues where the entity was mentioned.  The model will enable decision makers understand the sentiments expressed  towards an entity. It also estimates online  visibility of the entity based on its number of followers and the accounts’  lifespan. The perceived sentiments will aid better  decisions that could advance loyalty to the entity. Future studies  would examine other computational models to predict  various Twitter features that increases an entity's online visibility.