sentiment-analysis

问题 In this part of the documentation of the Google Cloud Platform Natural Language API, it is described that The overall score and magnitude values for an entity are an aggregate of the specific score and magnitude values for each mention of the entity. I can't figure out how this aggregation works. In the example provided in the documentation, Marvin Gaye has two mentions. One of the mentions has a sentiment of 0.4 and a magnitude of 0.4, the other mention has a score of -0.2 and a magnitude 0

问题 In this part of the documentation of the Google Cloud Platform Natural Language API, it is described that The overall score and magnitude values for an entity are an aggregate of the specific score and magnitude values for each mention of the entity. I can't figure out how this aggregation works. In the example provided in the documentation, Marvin Gaye has two mentions. One of the mentions has a sentiment of 0.4 and a magnitude of 0.4, the other mention has a score of -0.2 and a magnitude 0

问题 I'm doing sentiment analysis for financial articles. To enhance the accuracy of my naive Bayes classifier, I'd like to implement negation handling. Specifically, I want to add the prefix "not_" to the word following a "not" or "n't" So if there's something like this in my corpus: x <- "They didn't sell the company." I want to get the following: "they didn't not_sell the company." (the stopword "didn't" will be removed later) I could find only the gsub() function, but it doesn't seem to work

问题 I'm doing sentiment analysis for financial articles. To enhance the accuracy of my naive Bayes classifier, I'd like to implement negation handling. Specifically, I want to add the prefix "not_" to the word following a "not" or "n't" So if there's something like this in my corpus: x <- "They didn't sell the company." I want to get the following: "they didn't not_sell the company." (the stopword "didn't" will be removed later) I could find only the gsub() function, but it doesn't seem to work

问题 I'm doing sentiment analysis from twitter but my tweets are on Spanish so I can't use tidytext to classify the words. Does anyone know if there is a similar package for Spanish? 回答1: There are not a lot of good open source options for sentiment lexicons in non-English languages right now, unfortunately. You can request the NRC lexicon in other languages from the authors; it is translated by Google Translate (which of course adds uncertainty but has shown to be mostly OK overall) and the

问题 I'm doing sentiment analysis from twitter but my tweets are on Spanish so I can't use tidytext to classify the words. Does anyone know if there is a similar package for Spanish? 回答1: There are not a lot of good open source options for sentiment lexicons in non-English languages right now, unfortunately. You can request the NRC lexicon in other languages from the authors; it is translated by Google Translate (which of course adds uncertainty but has shown to be mostly OK overall) and the

问题 Trying to use Google Sentiment Analysis. Here is the code I got from the Google tutorial. """Demonstrates how to make a simple call to the Natural Language API.""" import argparse from google.cloud import language def print_result(annotations): score = annotations.sentiment.score magnitude = annotations.sentiment.magnitude for index, sentence in enumerate(annotations.sentences): sentence_sentiment = sentence.sentiment.score print('Sentence {} has a sentiment score of {}'.format( index,

问题 I have a (probably) stupid problem with score.sentiment I'm trying to use this function with 3 default phrases, the problem is that the function return score 0.0.0, but it should return 2.-5.4 I don't understand the problem because RGui don't give me errors and I'm following a tutorial! I've dowloaded lists for negative and positive words with hu.liu.pos = scan('https://www.dropbox.com/sh/3xctszdxx4n00xq/AAA_Go_Y3kJxQACFaVBem__ea/positive-words.txt?dl=0', what='character', comment.char=';');

问题 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? 回答1: You can visit the following blog shows in detail how do you calculate TFIDF. 回答2: You use the TF-IDF weights as features/predictors in your statistical model. I suggest to use either gensim [1]or

问题 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? 回答1: You can visit the following blog shows in detail how do you calculate TFIDF. 回答2: You use the TF-IDF weights as features/predictors in your statistical model. I suggest to use either gensim [1]or