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Sentiment Analysis

in this repo, a twitter dataset is going to analysed. dataset contains twitts and label of them. labels are "positiv, negative, neutral". but there is a imbalance that neutral twitts are more than others and the negative sentences are least. here i'm going to describe some things.

preprocessing

  1. removing stop words: stop words are those who has no special information and not important. but sometime this get important.
  2. tokenize: 2.1: word tokenize: splitting a sentense to words 2.2: splitt a text to sentences. it creates list of tokenized objects.
  3. lemmatize: converting verbs like "went, running" to "go, run".

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