Unsupervised sentiment Analysis using doc2vec

user_202375 0
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Folks,

I have searched Google for different type of papers/blogs/tutorials etc but haven't found anything helpful. I would appreciate if anyone can help me. Please note that I am not asking for code step-by-step but rather an idea/blog/paper or some tutorial.

Here's my problem statement:

Just like sentiment analysis is used for identifying positive and negative tone of a sentence, I want to find whether a sentence is forward-looking (future outlook) statement or not.

I do not want to use bag of words approach to sum up the number of forward-looking words/phrases such as "going forward", "in near future" or "In 5 years from now" etc. I am not sure if word2vec or doc2vec can be used. Please enlighten me.

Thanks.

word2vec doc2vec gensim sentiment-analysis nlp
1 Answers

It seems what you are interested in doing is finding temporal statements in texts.

Not sure of your final output, but let's assume you want to find temporal phrases or sentences which contain them.

One methodology could be the following:

  1. Create list of temporal terms [days, years, months, now, later]
  2. Pick only sentences with key terms
  3. Use sentences in doc2vec model
  4. Infer vector and use distance metric for new sentence
    • GMM Cluster + Limit
    • Distance from average

Another methodology could be:

  1. Create list of temporal terms [days, years, months, now, later]
  2. Do Bigram and Trigram collocation extraction
  3. Keep relevant collocations with temporal terms
  4. Use relevant collocations in a kind of bag-of-collocations approach
    • Matched binary feature vectors for relevant collocations
    • Train classifier to recognise higher level text

This sounds like a good case for a Bootstrapping approach if you have large amounts of texts.

Both are semi-supervised really, since there is some need for finding initial temporal terms, but even that could be automated using a word2vec scheme and bootstrapping

user_19348510
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