sentiment-analysis

I have implemented sentiment analysis using the sentiment analysis module of Lingpipe. I know that they use a Dynamic LR model for this. It just tells me if the test string is a positive sentiment or negative sentiment. What ideas could I use to determine the object for which the sentiment has been expressed? If the text is categorized as positive sentiment, I would like to get the object for which the sentiment has been expressed - this could be a movie name, product name or others. Although this question is really old but I would like to answer it for others' benefit. What you want here is

I'd like to apply qdap 's polarity function to a vector of documents, each of which could contain multiple sentences, and obtain the corresponding polarity for each document. For example: library(qdap) polarity(DATA$state)$all$polarity # Results: [1] -0.8165 -0.4082 0.0000 -0.8944 0.0000 0.0000 0.0000 -0.5774 0.0000 [10] 0.4082 0.0000 Warning message: In polarity(DATA$state) : Some rows contain double punctuation. Suggested use of `sentSplit` function. This warning can't be ignored, as it seems to add the polarity scores of each sentence in the document. This can result in document-level

I do have some positive and negative sentence. I want very simple to use Python NLTK to train a NaiveBayesClassifier for investigate sentiment for other sentence. I try to use this code, but my result is always positive. http://www.sjwhitworth.com/sentiment-analysis-in-python-using-nltk/ I am very new at python so there my be a mistake in the code when i copy it. import nltk import math import re import sys import os import codecs reload(sys) sys.setdefaultencoding('utf-8') from nltk.corpus import stopwords __location__ = os.path.realpath( os.path.join(os.getcwd(), os.path.dirname(__file__)))

I'm trying to implement the naive Bayes classifier for sentiment analysis. I plan to use the TF-IDF weighting measure. I'm just a little stuck now. NB generally uses the word(feature) frequency to find the maximum likelihood. So how do I introduce the TF-IDF weighting measure in naive Bayes? You can visit the following blog shows in detail how do you calculate TFIDF. You use the TF-IDF weights as features/predictors in your statistical model. I suggest to use either gensim [1]or scikit-learn [2] to compute the weights, which you then pass to your Naive Bayes fitting procedure. The scikit-learn

I am trying to find a way to implement the Affective Norms for English Words (in dutch) for a longitudinal sentiment analysis with Quanteda. What I ultimately want to have is a "mean sentiment" per year in order to show any longitudinal trends. In the data-set all words a scored on a 7-point Likert-scale by 64 coders on four categories, which provides a mean for each word. What I want to do is take one of the dimensions and use this to analyse changes in emotions over time. I realise that Quanteda has a function for implementing the LIWC-dictionary, but I would prefer using the open-source