Unsupervised Sentiment Analysis

Question

I\'ve been reading a lot of articles that explain the need for an initial set of texts that are classified as either \'positive\' or \'negative\' before a sentiment analysis

Answer 1

I tried several methods of Sentiment Analysis for opinion mining in Reviews. What worked the best for me is the method described in Liu book: http://www.cs.uic.edu/~liub/WebMiningBook.html In this Book Liu and others, compared many strategies and discussed different papers on Sentiment Analysis and Opinion Mining.

Although my main goal was to extract features in the opinions, I implemented a sentiment classifier to detect positive and negative classification of this features.

I used NLTK for the pre-processing (Word tokenization, POS tagging) and the trigrams creation. Then also I used the Bayesian Classifiers inside this tookit to compare with other strategies Liu was pinpointing.

One of the methods relies on tagging as pos/neg every trigrram expressing this information, and using some classifier on this data. Other method I tried, and worked better (around 85% accuracy in my dataset), was calculating the sum of scores of PMI (punctual mutual information) for every word in the sentence and the words excellent/poor as seeds of pos/neg class.