Twitter Sentiment Analysis using Dynamic Clusterig
Budget: ₹600 – ₹1,500 INR
We need to implement Dynamic Clustering so that we can form clusters of tweets on the basis of similarities among tweets.
The output of a dynamic clustering algorithm for tweets can include:
Cluster labels: Each tweet is assigned to a cluster label based on its similarity to other tweets in the cluster.
Cluster centroids: A representative tweet or centroid is computed for each cluster that summarizes the content of the tweets in that cluster.
Cluster statistics: Descriptive statistics such as the size of each cluster, the average sentiment score of tweets in each cluster, and other metrics can be computed for each cluster.
Visualization: The clusters can be visualized using techniques such as scatter plots, heatmaps, or word clouds, to help understand the distribution and characteristics of the clusters.
The output of a dynamic clustering algorithm for tweets can include:
Cluster labels: Each tweet is assigned to a cluster label based on its similarity to other tweets in the cluster.
Cluster centroids: A representative tweet or centroid is computed for each cluster that summarizes the content of the tweets in that cluster.
Cluster statistics: Descriptive statistics such as the size of each cluster, the average sentiment score of tweets in each cluster, and other metrics can be computed for each cluster.
Visualization: The clusters can be visualized using techniques such as scatter plots, heatmaps, or word clouds, to help understand the distribution and characteristics of the clusters.