CODEWITHSUNDEEP
textpredictionsentiment-analysis
Sirjun Sagarino
Sirjun Sagarino

Reputation: 1

how to get the comments on social media and make it as your data?

I've proposed a title for our thesis, Movie Success Prediction through Social Media comments using Sentiment Analysis, is there a way you can get the comments on social media (twitter, Instagram, Facebook etc.) and use it for your software? like an API or any other way. is that even possible to use your software on different social media to get the comments for prediction or should i change my title and stick to one social media like Facebook or twitter only?

what's the good algorithm for this? what programming language and framework/IDE should i use?

I've done lots of research on google and still hoping for more info here. Thank you.

Edit: I'll only use YouTube and YouTube API.

Upvotes: 0

Views: 361

Answers (1)

berkin
berkin

Reputation: 558

From the title of your question, it seems that the method you need to use is distant supervision. You need to retrieve data with labels you think it is proper for your task. For instance, a tweet containing #perfect hashtag would probably be a positive tweet. So, you can define set of hashtags for your task, negative, positive or even for neutral; then you can retrieve tweets by those via Twitter API. For your task, those should be for movies, therefore your data should contain movie related information in first place.

Given that you will deal with text data and you'd like to create your own dataset, it is better to start with Twitter. Its API works for your needs and it is very well-documented. The language and frameworks are upto your choice, since APIs supports many known languages as well. Personally, I'd start with python or java to quickly solve future problems easier with community support.

For a general survey of this area, you may dive into papers and resources from here:

https://scholar.google.com.tr/scholar?hl=en&q=distant+supervision+sentiment+analysis

Distant supervision could be used to create a sentiment lexicon out of millions English tweets by using sets of negative and positive hashtags as well. You may take a look at Chapter 5 of this thesis ( https://spectrum.library.concordia.ca/980377/1/Ozdemir_MCompSc_F2015.pdf ), this may also give a good insight for your thesis, too.

Hope this helps.

Cheers

Upvotes: 1

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