t1dus/topic-modelling

Gets a current topic based on captions

Topic-modelling

Gets a current topic based on captions using tf-idf.

Using

git clone https://github.com/T1duS/Topic-modelling.git python3 main.py input.txt

If you want better results, change the corpus.txt file and run python3 main,py input.txt -R -R restarts the preprocess

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Sample input

News is information about current events. This may be provided through many different media: word of mouth, printing, postal systems, broadcasting, electronic communication, or through the testimony of observers and witnesses to events. It is also used as a platform to manufacture opinion for the population see propaganda.

Common topics for news reports include war, government, politics, education, health, the environment, economy, business, fashion, and entertainment, as well as athletic events, quirky or unusual events. Government proclamations, concerning royal ceremonies, laws, taxes, public health, and criminals, have been dubbed news since ancient times. Humans exhibit a nearly universal desire to learn and share news, which they satisfy by talking to each other and sharing information. Technological and social developments, often driven by government communication and espionage networks, have increased the speed with which news can spread, as well as influenced its content. The genre of news as we know it today is closely associated with the newspaper, which originated in China as a court bulletin and spread, with paper and printing press, to Europe.

Sample output

News is information about current events.