I was wondering if anybody knew where I could obtain dictionaries of positive and negative words. I'm looking into sentiment analysis a开发者_开发知识库nd this is a crucial part of it.

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The Sentiment Lexicon, at the University of Pittsburgh might be what you are after. It's a lexicon of about 8,000 words with positive/neutral/negative sentiment. It's described in more detail in this paper and released under the GPL.

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Sentiment Analysis (Opinion Mining) lexicons

• MPQA Subjectivity Lexicon
• Bing Liu and Minqing Hu Sentiment Lexicon
• SentiWordNet (Included in NLTK)
• VADER Sentiment Lexicon
• SenticNet
• LIWC (not free)
• Harvard Inquirer
• ANEW

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Sources:

• Keenformatics - Sentiment Analysis lexicons and datasets (my blog)
• Hutto, C. J., and Eric Gilbert. "Vader: A parsimonious rule-based model for sentiment analysis of social media text." Eighth International AAAI Conference on Weblogs and Social Media. 2014.
• Sentiment Symposium Tutorial by Christopher Potts
• Personal experience

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Arriving a bit late I'll just note that dictionaries have a limited contribution for sentiment analysis. Some sentiment bearing sentences do not contain any "sentiment" word - e.g. "read the book" which could be positive in a book review while negative in a movie review. Similarly, the sentiment word "unpredictable" could be positive in the context of a thriller but negative when describing the breaks system of the Toyota.

and there are many more...

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Professor Bing Liu provide an English Lexicon of about 6800 word, you can download form this link: Opinion Mining, Sentiment Analysis, and Opinion Spam Detection

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This paper from 2002 describes an algorithm for deriving such a dictionary from text samples automatically, using only two words as a seed set.

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AFINN you can find here and also create it dynamically. Like whenever unknown +ve word comes add it with +1. Like banana is new +ve word and appearing twice then it will become +2.

As much articles and data you craws your dictionary would become stronger!

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The Harvard-IV dictionary directory http://www.wjh.harvard.edu/~inquirer/homecat.htm has at least two sets of ready-to-use dictionaries for positive/negative orientation.

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You can use vader sentiment lexicon

from nltk.sentiment.vader import SentimentIntensityAnalyzer

sentence='APPle is good for health'
sid = SentimentIntensityAnalyzer()
ss = sid.polarity_scores(sentence)  
print(ss)

it will give you the polarity of sentence.

output:

 {'compound': 0.4404, 'neu': 0.58, 'pos': 0.42, 'neg': 0.0}

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Sentiwords gives 155,000 words (and their polarity, that is, a score between -1 and 1 for very negative through to very positive). The lexicon is discussed here