sentiment-analysis

问题 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

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed last year . 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

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed last year . 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

问题 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? 回答1: To

问题 I am currently working on a project, a simple sentiment analyzer such that there will be 2 and 3 classes in separate cases . I am using a corpus that is pretty rich in the means of unique words (around 200.000). I used bag-of-words method for feature selection and to reduce the number of unique features , an elimination is done due to a threshold value of frequency of occurrence . The final set of features includes around 20.000 features, which is actually a 90% decrease , but not enough for

问题 Hell everyone! I'm using the Stanford Core NLP package and my goal is to perform sentiment analysis on a live-stream of tweets. Using the sentiment analysis tool as is returns a very poor analysis of text's 'attitude' .. many positives are labeled neutral, many negatives rated positive. I've gone ahead an acquired well over a million tweets in a text file, but I haven't a clue how to actually train the tool and create my own model. Link to Stanford Sentiment Analysis page "Models can be

问题 This question already has answers here : “Notice: Undefined variable”, “Notice: Undefined index”, and “Notice: Undefined offset” using PHP (28 answers) Closed 9 days ago . Hello everyone can help me, I have a problem "ErrorException (E_NOTICE) Undefined variable: class" class ControllerTraining extends Controller { public function index() { $title = "Data Training"; foreach(Sentimen::all() as $stm){ $class['class'][] = $stm->kategori; $data_training[$stm->kategori] = WordFrequency::where('id

问题 I am using NodeJs to create my application with the help of sentiment lib The problem is that it is giving wrong results when a negative word is used in a positive manner. var sentiment = require('sentiment'); var result = sentiment('I am dying to eat a kitkat!'); console.dir(result); { score: -3, comparative: -0.42857142857142855, tokens: [ 'i', 'am', 'dying', 'to', 'eat', 'a', 'kitkat' ], words: [ 'dying' ], positive: [], negative: [ 'dying' ] } ///or result = sentiment('your internet is

问题 Having some problems. I'm doing a TwitterSentimentAnalysis on a dataset of length 1.6 million. Since my pc could not do the work (due to so many computations), the professor told me to use the university server. I just realiazed that on the server, python version is 2.7 that it does not allow me to use the parameter encoding in csv reader for reading the file. Anytime I got the UnicodeDecodeError , I have to manually remove the tweet from the dataset otherwise I can't do the rest. I have

问题 This is an incredible resource. I cant believe how generous contributors to the platform are. I would be grateful for any advice on dealing with text analytics / Sentiment Analysis using Russian / Cyrillic languages. Syuzhet is my preferred tool - the opportunity to obtain sentiment across 8 emotions as well as negative and positive polarity is outstanding. However, i don't think it supports Cyrillic languages. Is there any alternative? 回答1: I was just trying to figure out the same thing: how