The sentiment analysis via the AFINN word list.
The sentiment of a topic is found by summing sentiment of the individual texts weighted by the number of texts in the topic.
The sentiment of each individual text is found by summing the
sentiment strength of each word weighted by the number of words.
The weighting is by the square root.
A word list excludes common English words ("stopwords")
The analysis will only work on up to a few hundreds short texts.
The results may change each time you run the algorithm.
This is due to random initializations and the issues in the
The value shown after each topic, each word and each document is the load telling how important they are.
Texts and words are assigned exclusively to one topic
even if some of the texts are load partially on two or more
If the load of some texts or words are zero then they show up in
the "not assigned" category.
The example texts are taken as the first (few) line(s) in
Wikipedia articles about companies and countries.
If the topic mining works well it should separate country and company