sentiment-analysis

I was using AlchemyAPI for text analysis. I want to know if there is way to influence the API results or fine-tune it as per the requirement. I was trying to analyse different call center conversations available on internet. To understand the sentiments i.e. whether customer was unsatisfied/angry and hence conversation is negative. For 9 out of 10 conversations it gave sentiment as positive and for 1 it was negative. That conversation was about emergency response system (#911 in US). It seems that words shooting, fear, panic, police, siren could have cause this result. But actually the whole

This is the first time I am building a sentiment analysis machine learning model using the nltk NaiveBayesClassifier in Python. I know it is too simple of a model, but it is just a first step for me and I will try tokenized sentences next time. The real issue I have with my current model is: I have clearly labeled the word 'bad' as negative in the training data set (as you can see from the 'negative_vocab' variable). However, when I ran the NaiveBayesClassifier on each sentence (lower case) in the list ['awesome movie', ' i like it', ' it is so bad'], the classifier mistakenly labeled 'it is

I need a list of positive and negative words with the weights assigned to words according to how strong and week they are. I have got : 1.) WordNet - It gives a + or - score for every word. 2.) SentiWordNet - Giving positive and negative values in the range [0,1]. I checked these on few words, love - wordNet is giving 0.0 for both noun and verb, I dont know why i think it should be positive by at least some factor. repress - wordNet gives -9.93 - SentiWordNet gives - 0.0 for both pos and neg. (should be negative) repose - wordNet - 2.488 - SentiWordNet - { pos - 0.125, neg - 0.5 } (should be

How do I add the tag NEG_ to all words that follow not , no and never until the next punctuation mark in a string(used for sentiment analysis)? I assume that regular expressions could be used, but I'm not sure how. Input: It was never going to work, he thought. He did not play so well, so he had to practice some more. Desired output: It was never NEG_going NEG_to NEG_work, he thought. He did not NEG_play NEG_so NEG_well, so he had to practice some more. Any idea how to solve this? To make up for Python's re regex engine's lack of some Perl abilities, you can use a lambda expression in a re.sub

I'm doing some sentiment analysis using SentiWordNet and I referred to the post here How to use SentiWordNet . However, I'm getting a score of 0.0 despite trying out various inputs. Is there anything I'm doing wrong here? Thanks! import java.io.BufferedReader; import java.io.File; import java.io.FileReader; import java.util.HashMap; import java.util.Iterator; import java.util.Set; import java.util.Vector; public class SWN3 { private String pathToSWN = "C:\\Users\\Malcolm\\Desktop\\SentiWordNet_3.0.0\\home\\swn\\www\\admin\\dump\\SentiWordNet_3.0.0.txt"; private HashMap<String, Double> _dict;

I am looking on standford corenlp using the Named Entity REcognizer.I have different kinds of input text and i need to tag it into my own Entity.So i started training my own model and it doesnt seems to be working. For eg: my input text string is "Book of 49 Magazine Articles on Toyota Land Cruiser 1956-1987 Gold Portfolio http://t.co/EqxmY1VmLg http://t.co/F0Vefuoj9Q " I go through the examples to train my own models and and look for only some words that I am interested in. My jane-austen-emma-ch1.tsv looks like this Toyota PERS Land Cruiser PERS From the above input text i am only interested