Twitter Sentiment Analysis Using doc2vec

Sergey Bryl uses word2vec and doc2vec to perform Twitter sentiment analysis in R:

But doc2vec is a deep learning algorithm that draws context from phrases. It’s currently one of the best ways of sentiment classification for movie reviews. You can use the following method to analyze feedbacks, reviews, comments, and so on. And you can expect better results comparing to tweets analysis because they usually include lots of misspelling.

We’ll use tweets for this example because it’s pretty easy to get them via Twitter API. We only need to create an app on https://dev.twitter.com (My apps menu) and find an API Key, API secret, Access Token and Access Token Secret on Keys and Access Tokens menu tab.

Click through for more details, including code samples.

Related Posts

Python versus R (Again)

Alex Woodie looks at whether Python is dominating R in the data science space: There is some evidence that Python’s popularity is hurting R usage. According to the TIOBE Index, Python is currently the third most popular language in the world, behind perennial heavyweights Java and C. From August 2018 to August 2019, Python usage surged […]

Read More

Local Randomness and R

Evgeni Chasnovski has a problem around generating random data: Let’s say we have a deterministic (non-random) problem for which one of the solutions involves randomness. One very common example of such problem is a function minimization on certain interval: it can be solved non-randomly (like in most methods of optim()), or randomly (the simplest approach being […]

Read More

Categories

February 2017
MTWTFSS
« Jan Mar »
 12345
6789101112
13141516171819
20212223242526
2728