Timeline for sentiment analysis nltk python
Current License: CC BY-SA 4.0
5 events
| when toggle format | what | by | license | comment | |
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| Oct 22, 2018 at 13:46 | comment | added | Stephen Witkowski | I'd recommend you do a little more research on what's already out there. From what I can tell, you're trying to reinvent the wheel. Read up on lexicons. These are 'word score' score databases. There are some that look at unigrams (single words), others look at bigrams, or trigrams. If you really need to, you can add onto these lexicons with your own words, but I expect this will suffice. | |
| Oct 21, 2018 at 7:45 | comment | added | nikitok56 | Thanks a lot! Yeah, I used easy sentiments such as "good/bad" but I'd like to use these training set applied to a massive of sophisticated sentiments. So you suggesting me collecting words and make score as a value? I understand that it can be done this way but I thought there can be another solution without giving each word a score? | |
| Oct 16, 2018 at 13:19 | comment | added | Stephen Witkowski | What might be more what you're looking to do is predict a score based on the sentiment of a review. Maybe you can look at the sentiment of the movie review and try to predict the score given by that user (if that data is available). It seems like you're trying to predict sentiment, when it's something you can easily discern from the information available. Now, if you want to create a custom sentiment score (using slang for example), then that's another task. You'd need to build a dictionary of words and associate a score with each word. | |
| Oct 15, 2018 at 17:24 | comment | added | nikitok56 | I'have already took some NLTK courses, and someone suggested trying VADER. As fro NLTK, I tried to do something similar to movie review like finding out how each sentiment is negative/positive, but then I got stuck in this project. Also, it seems to me that ML approach is more perspective and beneficial. | |
| Oct 12, 2018 at 20:01 | history | answered | Stephen Witkowski | CC BY-SA 4.0 |