Classifiers

[omna]

1. Simple linear classifier: Uses the weights of the words derived from (SentiWordNet + Synonyms) to classify the tweets as positive or negative. Example: tweet: happy cake it averages the positive values of happy and cake, and the same for negative values. then find out how positive or negative the tweet as a whole is. For new words appearing in the test data, the overall positive/negative score is given to the tweet, if the majority of the words included in it are already in the vocabulary.
2. Naive Bayes classifier. The training data of N tweets. For training data, the tweets are classified manually. Then the new words present in the tweets are processed individually and a probability is assigned to it. Learn new words and add in the vocabulary.
3. Both together. Ensemble of classifiers. For agreed results, no learning and straightforward classification. But for conflicted results, iterations and learning happens.

Which of the three classification methods most efficient? (Precision, recall)

[croo]

1. done
2. done

Both are able to tell the calcualted probability of the tweet being negative and positive, and tell it's class.

1. In progress.