NLP and Machine learning for sentiment analysis [closed]

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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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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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Before starting from scratch, you can have a look at existing NLP frameworks.

• Gate
• OpenNLP
• ...

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You can look at the software WEKA. It has many built-in machine learning classifiers which you can use for sentiment classification. It requires you to convert the input data to ARFF format.

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You could find some interesting datasets from NLP, NER to Image Classification, Bounding here: https://dataturks.com/projects/trending

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If You are a completely beginner for nlp and python , then you can try some good api for sentiment analysis.

Here are some Api that you can use for your task

1.) sentiment analysis api

2.) Monkey Learn api for sentiment analysis

For reading Purpose Great info on Sentiment Analysis: