Abstract
Twitter is a social media platform where users can post, read, and interact with 'tweets'. Third party like corporate
organization can take advantage of this huge information by collecting data about their customers' opinions. The use of
emoticons on social media and the emotions expressed through them are the subjects of this research paper. The purpose
of this paper is to present a model for analyzing emotional responses to real-life Twitter data. The proposed model is
based on supervised machine learning algorithms and data on has been collected through crawler “TWEEPY” for
empirical analysis. Collected data is pre-processed, pruned and fed into various supervised models. Each tweet is
assigned to sentiment based on the user's emotions, positive, negative, or neutral.
organization can take advantage of this huge information by collecting data about their customers' opinions. The use of
emoticons on social media and the emotions expressed through them are the subjects of this research paper. The purpose
of this paper is to present a model for analyzing emotional responses to real-life Twitter data. The proposed model is
based on supervised machine learning algorithms and data on has been collected through crawler “TWEEPY” for
empirical analysis. Collected data is pre-processed, pruned and fed into various supervised models. Each tweet is
assigned to sentiment based on the user's emotions, positive, negative, or neutral.
Keywords
Bag of words (BOW) model
machine learning
Naive Bayes (NB)
Opinion Mining
Sentiment analysis
Support Vector Machine (SVM)
TF-IDF model
Twitter
Keywords
تويتر، تحليل المشاعر، استخراج الآراء، التعلم الآلي، خوارزميات بايز الساذجة (NB)، آلة الدعم المتجهي (SVM)، نموذج حقيبة الكلمات (BOW)، نموذج TF-IDF