SudachiPy

Tokenize the tweet using SudachiPy with a user dictionary for slang.

Identify the part-of-speech tags for each token.

Use a sentiment lexicon to determine the sentiment score for each token.

Aggregate the sentiment scores to determine the overall sentiment of the tweet.

Tokenize the news article using SudachiPy.

Identify the part-of-speech tags for each token.

Use a named entity recognition model to identify named entities based on the tokens and their context.

Extract the identified named entities.

Tokenize the Japanese text using SudachiPy.

Use a machine translation model to translate the tokens into English.

Reconstruct the translated text.

Tokenize the text using SudachiPy.

Identify the most important sentences based on token frequency and part-of-speech tags.

Generate a summary by combining the most important sentences.

Tokenize the document using SudachiPy.

Identify the part-of-speech tags for each token.

Filter out stop words and common words.

Calculate the frequency of the remaining tokens.

Extract the most frequent tokens as keywords.