使用隐马尔可夫模型进行情感分析

Question

I have a list of reviews, each element of the list is a review of IMDB data set in kaggle. there are 25000 reviews in total. I have the label of each review +1 for positive and -1 for negative.

I want to train a Hidden Markov Model with these reviews and labels.

1- what is the sequence that I should give to HMM? is it something like Bag of words or is it something else like probabilities which I need to calculate? what kind of feature extraction method is appropriate? I was told to use Bag of words on review's list, but when I searched a little I find out HMM cares about the order but bag of words doesn't maintain the order of words in sequences. how should I prepare this List of reviews to be able to feed it into a HMM model?

2- is there a framework for this? I know hmmlearn, and I think I should use the MultinomialHMM, correct me if I'm wrong. but it is not supervised, its models do not take labels as input when i want to train it, and I get some funny errors which I don't know how to solve because of the first question I asked about the correct type of input I should give to it. seqlearn is the one I find recently, is it good or there is a better one to use?

I appreciate any guidance since I have almost zero knowledge about NLP.

Answer 1

I was able to do it somehow with surprisingly good accuracy, yet I am not sure what happened exactly, I used seqlearn framework which has a sad documentation. I really suggest to use MATLAB instead of python for HMM.

I used sklearn TfidfVectorizer for feature extraction, then I did this:

vectorizer = TfidfVectorizer(norm=None)
x_train = vectorizer.fit_transform(train_review)
x_test = vectorizer.transform(test_review)

len_train_seq = np.array([[1,1]]*(len(train_review)/2))
len_test_seq = np.array([1]*len(test_review))

model = seqlearn.hmm.MultinomialHMM()
HMM_Classifier = model.fit(x_train, Y, lengths = len_train_seq)
y_predict = HMM_Classifier.predict(x_test, lengths=len_test_seq)

I still would appreciate if a knowledgable person about HMM gives a more robust and clean guideline about doing sentiment analysis with HMM.