493 views
0 0 votes

I am trying to create a sentiment analysis model using binary classification as loss.I have a batch of tweets that some of them are tagged as positive (labeled as 1) and negative (labeled as 0).I manage to gather some tweets that are tagged as neutral but there are less  tweets than positive and negative.My thinking is to tag them with 0.5 to balance the classification probability.Is this legit?

 
0% Accept Rate Accepted 0 answers out of 1 questions

Please log in or register to answer this question.

Related questions

0 0 votes
0 0 answers
500
500 views
ntonis asked Jan 30, 2021
500 views
I am trying to create a sentiment analysis model and I have a question.After I preprocessed my tweets and created my vocabulary I've noticed that I have words that appear...
0 0 votes
0 0 answers
458
458 views
patmull asked Feb 11, 2021
458 views
I need some tool to classify articles based on short category text which consists of two or three words separated by '-'. The RSS/XML tag content is for example:Foreign -...
2 2 votes
1 answers 1 answer
4.9k
4.9k views
tofighi asked Jun 26, 2019
4,857 views
We want to use Naive Bayes for tagging documents. It is a classification task that we want to assign a class (tag) to each string. We currently have two tags: Sport and N...
1 1 vote
1 1 answer
457
457 views
ntonis asked Apr 10, 2020
457 views
How should i preprocess my data if i am gonna use a pretrainned word embedding like glove or word2vec?Should I use stemming or stopword removal techniques?
0 0 votes
0 0 answers
373
373 views
ivymelissa asked Jan 3, 2021
373 views
I've been researching sentiment analysis with word embeddings. I read papers that state that word embeddings ignore sentiment information of the words in the text. One pa...