-If it's positive or negative, neutralize it, by adding/removing words from the tweet i.e. semantics is same as original.
also there is no any training data available for neutral tweets.
For example: Input: “I like you as friend”
Output should be : “I consider you as friend”
Although your question is not clear, all I can say is that, in your example, both sentences are carrying exactly the same emotion type and count.
HTH.
Dr. Samer Sarsam
Samer Sarsam sir,
in sentiment analysis we are classifying in 3 classes i.e. positive , negative and neutral.
if we have positive or negative sentiments we have to convert it into neutral by adding or removing some words from sentence. But the meaning of words remain same, as i ask.
in given example input is having positive sentiment
and output is somehow neutral.
You need to be clear on what level this sentiment analysis will be performed on the tweets, e.g. word level, tweet level (sentence level), or topic level. The collection of neutral words can be obtained by the classifier depending on your selected approach and sentiment scoring techniques. My current research uses 7 emotion classifications, instead of three, by incorporating the lexicon based approach and term frequency for the sentiment scoring. The neutral words (non-sentiment words) are taken into account.
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