text-analysis

问题 Can we customize the Named Entity Recognition (NER) model in Azure ML Studio with a separate training dataset? What I want to do is to find out non-English names from a text. (Training dataset includes the set of names that going to use for training) 回答1: Unfortunately, this module's ability to perform NER with a custom set of entities is planned for the future, but not currently available. If you're familiar with Python and willing to put in the extra footwork, you might consider using the

问题 For a text analysis program, I would like to analyze the co-occurrence of certain words in a text. For example, I would like to see that e.g. the words "Barack" and "Obama" appear more often together (i.e. have a positive correlation) than others. This does not seem to be that difficult. However, to be honest, I only know how to calculate the correlation between two numbers, but not between two words in a text. How can I best approach this problem? How can I calculate the correlation between

来源： https://stackoverflow.com/questions/40783383/error-using-langdetect-in-python-no-features-in-text

问题 I am working on a R project. The data set I used is available at the following link https://www.kaggle.com/ranjitha1/hotel-reviews-city-chennai/data The code I have used is. df1 = read.csv("chennai.csv", header = TRUE) library(tidytext) tidy_books <- df1 %>% unnest_tokens(word,Review_Text) Here Review_Text is the text column. Yet, I get the following error. Error in check_input(x) : Input must be a character vector of any length or a list of character vectors, each of which has a length of 1.

问题 I am working on a R project. The data set I used is available at the following link https://www.kaggle.com/ranjitha1/hotel-reviews-city-chennai/data The code I have used is. df1 = read.csv("chennai.csv", header = TRUE) library(tidytext) tidy_books <- df1 %>% unnest_tokens(word,Review_Text) Here Review_Text is the text column. Yet, I get the following error. Error in check_input(x) : Input must be a character vector of any length or a list of character vectors, each of which has a length of 1.

问题 I am working on a R project. The data set I used is available at the following link https://www.kaggle.com/ranjitha1/hotel-reviews-city-chennai/data The code I have used is. df1 = read.csv("chennai.csv", header = TRUE) library(tidytext) tidy_books <- df1 %>% unnest_tokens(word,Review_Text) Here Review_Text is the text column. Yet, I get the following error. Error in check_input(x) : Input must be a character vector of any length or a list of character vectors, each of which has a length of 1.

问题 This question already has answers here : Closed 7 years ago . Possible Duplicate: Text Classification into Categories I am currently working on a solution to get the type of food served in a database with 10k restaurants based on their description. I'm using lists of keywords to decide which kind of food is being served. I read a little bit about machine learning but I have no practical experience with it at all. Can anyone explain to me if/why it would a be better solution to a simple

问题 Hi I am trying to do a sentiment analysis using Naive Bayes classifier in python 2.x. It reads the sentiment using a txt file and then gives output as positive or negative based on the sample txt file sentiments. I want the output the same form as input e.g. I have a text file of lets sat 1000 raw sentiments and I want the output to show positive or negative against each sentiment. Please help. Below is the code i am using import math import string def Naive_Bayes_Classifier(positive,

问题 I have a classic NLP problem, I have to classify a news as fake or real. I have created two sets of features: A) Bigram Term Frequency-Inverse Document Frequency B) Approximately 20 Features associated to each document obtained using pattern.en (https://www.clips.uantwerpen.be/pages/pattern-en) as subjectivity of the text, polarity, #stopwords, #verbs, #subject, relations grammaticals etc ... Which is the best way to combine the TFIDF features with the other features for a single prediction?

问题 I'm trying to do some text analysis to determine if a given string is... talking about politics. I'm thinking I could create a neural network where the input is either a string or a list of words (ordering might matter?) and the output is whether the string is about politics. However the brain.js library only takes inputs of a number between 0 and 1 or an array of numbers between 0 and 1. How can I coerce my data in such a way that I can achieve the task? 回答1: new brain.recurrent.LSTM(); this