I would like to finish my project within a month:
The project involves a hybrid method for sentiment analyiss of tweets written in Turkish language:
There are two ways in any of whom a kind of machine learning approach had to be applied.
A simple solution is below,but it relies heavily on NLTK library which works on only English texts:
github page of satyakalagara Twitter-Sentiment-Analysis twitter_sentiment_analysis phyton code
One way:information gathered from movie review sentiment could be used as a training data to test the sentiment of tweets gathered by hashtag.
The other way:naive bayes or support vector machines (producing features) could be edited to better analyze the sentiments (increase in accuracy).
As an example 60 percent accuracy rate of naive bayes could be raised to 80 percent accuracy by hybrid method.
note:
coder should be familiar with machine learning algorithms like suport vector machines
dataset could be either movie review datasets in (a specific topic) Turkish or random twitter messages in Turkish
i attached all.txt including positive, negative and neutral reviews about movies in order(seperated by blank line)
we need to improve classical machine learning algorithms as hybrid one i have a scores table of 15000 words obtained from sentiwordnet could be used as an initial dictionary to produce new features to be added to ml algorithms' features
The project involves a hybrid method for sentiment analyiss of tweets written in Turkish language:
There are two ways in any of whom a kind of machine learning approach had to be applied.
A simple solution is below,but it relies heavily on NLTK library which works on only English texts:
github page of satyakalagara Twitter-Sentiment-Analysis twitter_sentiment_analysis phyton code
One way:information gathered from movie review sentiment could be used as a training data to test the sentiment of tweets gathered by hashtag.
The other way:naive bayes or support vector machines (producing features) could be edited to better analyze the sentiments (increase in accuracy).
As an example 60 percent accuracy rate of naive bayes could be raised to 80 percent accuracy by hybrid method.
note:
coder should be familiar with machine learning algorithms like suport vector machines
dataset could be either movie review datasets in (a specific topic) Turkish or random twitter messages in Turkish
i attached all.txt including positive, negative and neutral reviews about movies in order(seperated by blank line)
we need to improve classical machine learning algorithms as hybrid one i have a scores table of 15000 words obtained from sentiwordnet could be used as an initial dictionary to produce new features to be added to ml algorithms' features
