Sentiment analysis plays a crucial role in understanding public opinion and sentiment towards various topics. This paper presents an analysis of the Sentiment140 dataset using two clustering
techniques: kMeans and agglomerative clustering. The dataset consists of 1,600,000 tweets anno- tated with sentiment labels. The goal of this study is to explore the effectiveness of these clustering
algorithms in identifying sentiment patterns in the text data. The dataset is preprocessed to ensure data quality and compatibility with the clustering algorithms. The implementation plan involves applying both kMeans and agglomerative clustering on the dataset and evaluating their performance
using appropriate metrics. The expected results include the identification of distinct sentiment clus- ters and an assessment of the clustering algorithms’ effectiveness in sentiment analysis. This study
contributes to the understanding of sentiment analysis techniques and their application in real-world datasets.