COVID-19 Sentiment Analysis using K-Means and DBSCAN
Smitesh D. Patravali1, Siddu P. Algur2
1Smitesh D. Patravali, Research Scholar, Vijayanagara Sri Krishnadevaraya University, Ballari, Karnataka, India.
2Dr. Siddu P. Algur, Ex. Vice Chancellor, Vijayanagara Sri Krishnadevaraya University, Ballari, Karnataka, India.
Manuscript received on 07 August 2023 | Revised Manuscript received on 10 October 2023 | Manuscript Accepted on 15 November 2023 | Manuscript published on 30 November 2023 | PP: 12-17 | Volume-11 Issue-12, November 2023 | Retrieval Number: 100.1/ijese.L255811111223 | DOI: 10.35940/ijese.L2558.11111223
Open Access | Editorial and Publishing Policies | Cite | Zenodo | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The analysis of sentiment towards COVID-19 plays a crucial role in understanding public opinion. This research paper proposes sentiment analysis using K-means and DBSCAN clustering algorithms on the dataset of tweets related to COVID-19. Pre-processing and extraction of features is carried out using Term Frequency-Inverse Document Frequency (Tf-idf) to capture the weight of words in the dataset. K-means clustering is explored to group similar sentiments together, enabling the identification of sentiment clusters related to COVID-19. The DBSCAN algorithm is then employed to identify outliers and noise in the sentiment clusters. The evaluation metrics considered were accuracy, recall, F1-score, and precision. It was observed that DBSCAN was more effective in identifying underlying patterns in the data more accurately.
Keywords: COVID-19, K-Means, DBSCAN, social media, Public Opinion.
Scope of the Article: Clustering