Twitter Sentimental Analysis based on Ordinal Regression

Abstract

For associations and people with a profound social, political, or monetary Sinterest in keeping up and fortifying their clout and notoriety, Twitter has become a goldmine. Sentiment analysis is the way toward characterizing and classifying the considerations and sentiments communicated in a source record. By performing this assessment investigation in a meticulous space, it is feasible to decide the force of area data on notion order. For feeling examination order, the proposed system utilizes the calculations Support Vector Regression (SVR), Decision Trees (DTs), and Random Forest (RF). The real execution of this structure depends on a twitter dataset unveiled by the NLTK corpora devices. The proposed approach will precisely identify ordinal relapse utilizing AI procedures.

Dwell Time Estimation of Import Containers as an Ordinal Regression Problem

“…Recent studies have solved similar problems with ordinal regressors based on deep neural networks and other machine-learning algorithms, e.g., image ordinal estimation [21], knee osteoarthritis severity [22], degree of building damage [23], and Twitter sentimental analysis [24]. These problems present a class attribute with an ordinal domain, such as the dwell time of import containers in a yard.…”