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I'm trying to write a program that takes text(article) as input and outputs the polarity of this text, weather its a positive or a negative sentiment. I've read extensively about different approaches but i am still confused. I read about many techniques like classifiers and machine learning. I would like direction and clear instructions on where to start. For example, i have a classifier which requires a dataset but how do i convert the text(article) into a dataset for the classifier. If anyone can tell me the logical sequence to approach this problem that would be greet. Thanks in advance! PS: please mention any related algorithms or open-source implementation
Regards, Mike
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Sentiment analysis (or opinion mining) is a natural language processing (NLP) technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.
RNNs are probably the most commonly used deep learning models for NLP and with good reason. Because these networks are recurrent, they are ideal for working with sequential data such as text. In sentiment analysis, they can be used to repeatedly predict the sentiment as each token in a piece of text is ingested.
Sentiment Analysis (also known as opinion mining or emotion AI) is a sub-field of NLP that tries to identify and extract opinions within a given text across blogs, reviews, social media, forums, news etc.
Sentiment analysis is a type of machine learning tool. Machine learning works with natural language processing to make up the core building blocks of the sentiment analysis process.
If you're using Python, I'd suggest you have a look at NLTK and the NLTK book.
This blog: streamhacker.com has some very good articles to get you started.
There's been lots of research in this area in the since the late 2000's.
UPDATE (Oct 2013):
Stanford researches made a breakthrough in sentiment analysis that has achieved more than 85% accuracy on average. (http://gigaom.com/2013/10/03/stanford-researchers-to-open-source-model-they-say-has-nailed-sentiment-analysis/)
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