TY - GEN
T1 - Comparative Study of Sentiment Analysis on Cyber Security Related Multi-sourced Data in Social Media Platforms
AU - Kapur, Keshav
AU - Harikrishnan, Rajitha
AU - Raghavendra, S.
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - The comparative study investigates cyber security attitudes and behaviour across time using data gathered from social networking platforms such as Reddit and Twitter. Due to the continuously advancing technology in the modern world, enormous amounts of data are produced every second. The chosen field of study seeks to determine the opinions of people on social media posts. The dataset for the proposed work was a multi-source dataset from the comment or post section of various social networking sites like Twitter, Reddit, etc. Natural Language Processing Techniques were incorporated to perform sentiment analysis on the obtained dataset. The proposed work provides a comparative analysis using various techniques namely ML, DL, and lexicon-based. Naive Bayes Classifier was used as a Machine Learning algorithm, TextBlob is used as the Lexicon-based approach, and the deep-learning algorithm used is BiLSTM. It was observed that BiLSTM model outperforms the other two models with an F1-score of 0.895, accuracy of 0.896, precision of 0.899, and recall of 0.891.
AB - The comparative study investigates cyber security attitudes and behaviour across time using data gathered from social networking platforms such as Reddit and Twitter. Due to the continuously advancing technology in the modern world, enormous amounts of data are produced every second. The chosen field of study seeks to determine the opinions of people on social media posts. The dataset for the proposed work was a multi-source dataset from the comment or post section of various social networking sites like Twitter, Reddit, etc. Natural Language Processing Techniques were incorporated to perform sentiment analysis on the obtained dataset. The proposed work provides a comparative analysis using various techniques namely ML, DL, and lexicon-based. Naive Bayes Classifier was used as a Machine Learning algorithm, TextBlob is used as the Lexicon-based approach, and the deep-learning algorithm used is BiLSTM. It was observed that BiLSTM model outperforms the other two models with an F1-score of 0.895, accuracy of 0.896, precision of 0.899, and recall of 0.891.
UR - http://www.scopus.com/inward/record.url?scp=85161135148&partnerID=8YFLogxK
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U2 - 10.1007/978-981-99-2264-2_7
DO - 10.1007/978-981-99-2264-2_7
M3 - Conference contribution
AN - SCOPUS:85161135148
SN - 9789819922635
T3 - Communications in Computer and Information Science
SP - 88
EP - 97
BT - Applications and Techniques in Information Security - 13th International Conference, ATIS 2022, Revised Selected Papers
A2 - Prabhu, Srikanth
A2 - Pokhrel, Shiva Raj
A2 - Li, Gang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Conference on Applications and Techniques in Information Security, ATIS 2022
Y2 - 30 December 2022 through 31 December 2022
ER -