International Travel Visa Topology — Hive Plot Visualization
Janu Verma Jverma
Visa Refused
Emoji Timeline of cultural references in Haruki Murakami's The Elephant Vanishes with on click descrrption.
Data source: Reddit discussion compiled by MufasaSimba
Exploration of K-Means clustering for various projection methods
Example studied is that of extracting most informative sentences from a textual document for auto summarization. Uses the LexRank algorithm. A graph G is created where :
- Nodes: All sentences of the document.
- Edges: There is an edge between two nodes if the frequency vectors of the corresponding sentences have (cosine) similarity above a threshold. For more details on SentenceGraph, refer to TextGraphics
LexRank asserts that PageRank (now called LexRank) scores of sentences in such a graph can be used to rank sentences in the order of their relevance to the document. And in turn, can be used for generating a summary of teh document.
I wrote a blog post for packtpub describing the K-means clustering procedure. The codes I provided were written in R, I also pointed to another blog post of mine where I use python to explain this algotithm. I decided to write a D3 visualization of the clusters.
Interactive line chart showing the time series of the total sentiment scores of the tweets realted to a topic extracted using search API. Sentiment on a day is the average of the sentiment scores of all the tweets realted to the given topic extracted that day.
Scatterplot of sentiment scores of tweets extracted using search API.
The sentiment is calculated using AFINN scores. For more information see Twitter Sentiment Analysis
It can be seen that a lot of the tweets are neutral (zero sentiment score).