Earticle

초록

영어

Sentiment Analysis is a crucial area of study within the realm of Computer Science. With the rapid advancement of Information Technology and the prevalence of social media, a substantial volume of textual comments has emerged on web platforms and social networks such as Twitter. Consequently, individuals have become increasingly active in disseminating both general and politically-related information, making it imperative to examine public responses. Many researchers have harnessed the unique features and content of social media to assess and forecast public sentiment regarding political events. This study presents an analytical investigation employing data from general discussions on Twitter to decipher public sentiment regarding the crisis in Pakistan. It involves the analysis of tweets authored by various ethnic groups and influential figures using Machine Learning techniques like the Support Vector Classifier (SVC), Decision Tree (DT), Naïve Bayes (NB) and Logistic Regression. Ultimately, a comparative assessment is conducted based on the outcomes obtained from different models in the experiments.

목차

Abstract
I. INTRODUCTION
II. DATASET
A. Data Collection
B. Data Labeling
III. TECHNIQUES
A. Support Vector Machine (SVM)
B. Decision Tree
C. Nȁive Bayes
D. Feature Extraction using TF/IDF
E. Bag of Words (BoW)
IV. IMPLEMENTATION
A. Text Preprocessing
B. Lexical sentiment analysis
C. Evaluation
V. EXPERIMENTAL RESULTS
REFERENCES

저자

• Faisal Shehzad [ Faculty of Computing, The Islamia University of Bahawalpur. Bahawalpur, Pakistan ]
• Muhammad Asad Ullah [ Department of Information Technology, Faculty of Computing, The Islamia University of Bahawalpur. Bahawalpur, Pakistan ]
• Muhammad Adnan Khan [ Department of Computing, Skyline University College. Sharjah, United Arab Emirates. ] Corresponding Author
• Nouh Sabri Elmitwally [ School of Computing and Digital Technology, Birmingham City University, Birmingham ]

참고문헌