Context: Sentiment analysis is the task of computationally labeling whether the content of text is positive or negative. One common approach to this is to compile lists of words which have a positive connotation (like “wonderful”, “lovely” and “best”) and a negative connotation (like “bad”, “horrible” or “awful”). Then you count how many positive and how many negative Content: This dataset contains three lists of Thai words: * swear words (94 words) * positive words (512 words) * negative words (1218 words) Each list a .txt file with one word per line. The character encoding is UTF-8. Acknowledgements: This dataset was compiled by [Wannaphong Phatthiyaphaibun](https://github.com/wannaphongcom) and is reproduced here under a CC-BY-SA 4.0 license. (You may also be interested in his translation of Python 3 documentation into Thai on [this blog](https://github.com/wannaphongcom/lexicon-thai/tree/master/sentiment).) Inspiration: * Can you analyze the sentiment in [this corpus of Thai](https://www.kaggle.com/rtatman/hse-thai-corpus)? Is there a difference in sentiment between the WIkipedia and Government parts of the corpus?