TY - GEN
T1 - HiSAT
T2 - Intelligent Systems Conference, IntelliSys 2022
AU - Kommu, Amrutha
AU - Patel, Snehal
AU - Derosa, Sebastian
AU - Wang, Jiayin
AU - Varde, Aparna S.
N1 - Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Social media websites such as Twitter have become so indispensable today that people use them almost on a daily basis for sharing their emotions, opinions, suggestions and thoughts. Motivated by such behavioral tendencies, the purpose of this study is to define an approach to automatically classify the tweets on Twitter data into two main classes, namely, hate speech and non-hate speech. This provides a valuable source of information in analyzing and understanding target audiences and spotting marketing trends. We thus propose HiSAT, a Hierarchical framework for Sentiment Analysis on Twitter data. Sentiments/opinions in tweets are highly unstructured-and do not have a proper defined sequence. They constitute a heterogeneous data from many sources having different formats, and express either positive or negative, or neutral sentiment. Hence, in HiSAT we conduct Natural Language Processing encompassing tokenization, stemming and lemmatization techniques that convert text to tokens; as well as Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) techniques that convert text sentences into numeric vectors. These are then fed as inputs to Machine learning algorithms within the HiSAT framework; more specifically, Random Forest, Logistic Regression and Naïve Bayes are used as text-binary classifiers to detect hate speech and non-hate speech from the tweets. Results of experiments performed with the HiSAT framework show that Random Forest outperforms the others with a better prediction in estimating the correct labels (with accuracy above the 95% range). We present the HiSAT approach, its implementation and experiments, along with related work and ongoing research.
AB - Social media websites such as Twitter have become so indispensable today that people use them almost on a daily basis for sharing their emotions, opinions, suggestions and thoughts. Motivated by such behavioral tendencies, the purpose of this study is to define an approach to automatically classify the tweets on Twitter data into two main classes, namely, hate speech and non-hate speech. This provides a valuable source of information in analyzing and understanding target audiences and spotting marketing trends. We thus propose HiSAT, a Hierarchical framework for Sentiment Analysis on Twitter data. Sentiments/opinions in tweets are highly unstructured-and do not have a proper defined sequence. They constitute a heterogeneous data from many sources having different formats, and express either positive or negative, or neutral sentiment. Hence, in HiSAT we conduct Natural Language Processing encompassing tokenization, stemming and lemmatization techniques that convert text to tokens; as well as Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) techniques that convert text sentences into numeric vectors. These are then fed as inputs to Machine learning algorithms within the HiSAT framework; more specifically, Random Forest, Logistic Regression and Naïve Bayes are used as text-binary classifiers to detect hate speech and non-hate speech from the tweets. Results of experiments performed with the HiSAT framework show that Random Forest outperforms the others with a better prediction in estimating the correct labels (with accuracy above the 95% range). We present the HiSAT approach, its implementation and experiments, along with related work and ongoing research.
KW - Bayesian models
KW - Knowledge discovery
KW - Logistic Regression
KW - NLP
KW - Opinion mining
KW - Random Forest
KW - Social media
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85138002632&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-16072-1_28
DO - 10.1007/978-3-031-16072-1_28
M3 - Conference contribution
SN - 9783031160714
T3 - Lecture Notes in Networks and Systems
SP - 376
EP - 392
BT - Intelligent Systems and Applications - Proceedings of the 2022 Intelligent Systems Conference IntelliSys Volume 1
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 1 September 2022 through 2 September 2022
ER -