sentiment-analysis
NLTK and Stopwords Fail #lookuperror
You don’t seem to have the stopwords corpus on your computer. You need to start the NLTK Downloader and download all the data you need. Open a Python console and do the following: >>> import nltk >>> nltk.download() showing info http://nltk.github.com/nltk_data/ In the GUI window that opens simply press the ‘Download’ button to download all … Read more
Unsupervised Sentiment Analysis
A classic paper by Peter Turney (2002) explains a method to do unsupervised sentiment analysis (positive/negative classification) using only the words excellent and poor as a seed set. Turney uses the mutual information of other words with these two adjectives to achieve an accuracy of 74%.
Feature Selection and Reduction for Text Classification
This is probably a bit late to the table, but… As Bee points out and you are already aware, the use of SVM as a classifier is wasted if you have already lost the information in the stages prior to classification. However, the process of text classification requires much more that just a couple of … Read more
Sentiment Analysis Dictionaries
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.
Training data for sentiment analysis [closed]
http://www.cs.cornell.edu/home/llee/data/ http://mpqa.cs.pitt.edu/corpora/mpqa_corpus You can use twitter, with its smileys, like this: http://web.archive.org/web/20111119181304/http://deepthoughtinc.com/wp-content/uploads/2011/01/Twitter-as-a-Corpus-for-Sentiment-Analysis-and-Opinion-Mining.pdf Hope that gets you started. There’s more in the literature, if you’re interested in specific subtasks like negation, sentiment scope, etc. To get a focus on companies, you might pair a method with topic detection, or cheaply just a lot of mentions of … Read more
Sentiment analysis for Twitter in Python [closed]
Good luck with that. Sentiment is enormously contextual, and tweeting culture makes the problem worse because you aren’t given the context for most tweets. The whole point of twitter is that you can leverage the huge amount of shared “real world” context to pack meaningful communication in a very short message. If they say the … Read more