sentiment-analysis

I plan on using Sentiwordnet 3.0 for Sentiment classification. Could someone clarify as to what the numbers associated with words in Sentiwordnet represent? For e.g. what does 5 in rank#5 mean? Also for POS what is the letter used to represent adverbs? Im assuming 'a' is adjectives. I could not find an explanation either on their site or on other sites. I found the answer. Seems like the number notation comes form Wordnet. It represents the rank in which the given word is commonly used. So rank#5 refers to the context in which rank is used 5th most commonly. Similarly rank#1 refers to the

I use the following to compute the sentiment of 200 short sentences. I did not use a training data set: for sentence in textblob.sentences: print(sentence.sentiment) The analysis returns two values: polarity and subjectivity. From what I read online, the polarity score is a float within the range [-1.0, 1.0] where 0 indicates neutral, +1 a very positive attitude and -1 a very negative attitude. The subjectivity is a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective. So, now my question: How are those scores computed? I have some zeros for the polarity

I am working on sentiment analysis and I am using dataset given in this link: http://www.cs.jhu.edu/~mdredze/datasets/sentiment/index2.html and I have divided my dataset into 50:50 ratio. 50% are used as test samples and 50% are used as train samples and the features extracted from train samples and perform classification using Weka classifier, but my predication accuracy is about 70-75%. Can anybody suggest some other datasets which can help me to increase the result - I have used unigram, bigram and POStags as my features. doxav There are many sources to get sentiment analysis dataset: huge

I have a django form, which is collecting user response. I also have a tensorflow sentences classification model. What is the best/standard way to put these two together. Details: tensorflow model was trained on the Movie Review data from Rotten Tomatoes. Everytime a new row is made in my response model , i want the tensorflow code to classify it( + or - ). Basically I have a django project directory and two .py files for classification. Before going ahead myself , i wanted to know what is the standard way to implement machine learning algorithms to a web app. It'd be awesome if you could