Published November 30, 2023 | Version CC-BY-NC-ND 4.0
Journal Open

COVID-19 Sentiment Analysis using K-Means and DBSCAN

Creators

  • 1. Research Scholar, Vijayanagara Sri Krishnadevaraya University, Ballari, Karnataka, India.

Contributors

Contact person:

  • 1. Research Scholar, Vijayanagara Sri Krishnadevaraya University, Ballari, Karnataka, India.
  • 2. Ex. Vice Chancellor, Vijayanagara Sri Krishnadevaraya University, Ballari, Karnataka, India.

Description

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.

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Additional details

Identifiers

DOI
10.35940/ijese.L2558.11111223
ISSN
2319-6378

Dates

Accepted
2023-11-15
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

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