Text mining on Amazon product review using R Program. I wasn't able to extract the particular product's review(i.e.If iphone 11 has 6k review, I need to extract all of it.) I'm getting only one column labelled x. Please let me know where I need to make necessary changes. I need those for performing sentiment analysis. install.packages("rvest") library(rvest) install.packages("xml2") library(xml2) install.packages("magrittr") library(magrittr) url <-"https://www.amazon.in/Apple-iPhone-11-128GB-Black/product-reviews/B07XVLW7YK/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews" apple <- NULL for(i in 1:100) { murl <- read_html(as.character(paste(url,i,sep = "="))) rev <- murl%>% html_nodes(".review-text")%>% html_text() …

I have a corpus of 23000 documents that need to be classified into 5 different categories. I do not have any labeled data available to me, just freeform text documents and labels(yes, one-word labels, not topics). So I followed a 2-step approach: Synthetically generate labeled data (using a rule-based labeling approach, obviously the recall is very low, ~ 1/8 documents are labeled) Somehow, use this labeled data to identify labels for other documents. I have attempted the following approaches for …

Consider the following code for obtaining term-document matrix for given texts import pandas as pd from sklearn.feature_extraction.text import CountVectorizer docs = ['why hello there', 'omg hello pony', 'she went there? omg'] vec = CountVectorizer() X = vec.fit_transform(docs) df = pd.DataFrame(X.toarray(), columns=vec.get_feature_names()) print(df) Here the docs list contain content of three text files. Now, i need to form the docs from three wiki pages: text #1, text #2, text #3 How can I perform the term document matrix from the links …

I have a labelled dataset of relevant and non-relevant documents for which I built a boolean document term matrix. I am trying to develop an algorithm which given this input would create a text-based boolean search rule which identifies a subset of the data favouring first of all sensitivity and then specificity. I'd like to know published literature on the topic. I made some initial search but couldn't find anything related. I'd be glad if you can point me to …

I have two different set of documents S1, S2, with 30 text documents each. Using some text representation method, such as tfidf and a distance measure, such as cosine similarity, I want to match similar documents from the two sets S1, S2. For example D1 from S1 is similar (say 0.36 similar ) to D28 from S2. My problem is that Tfidf.Vectorizer() creates an array of 30, 5000 for S1 and 30, 4500 for S2, with 30 rows for each …

I'm working with a bag of words in R: library(tm) corpus = VCorpus(textsource) dtm = DocumentTermMatrix(corpus) dtm = as.matrix(dtm) I use the matrix dtm to train a lasso model. Now I want to predict new (unseen) text. The problem is, that I need to generate a new dtm (for prediction) with the same matrix columns as the original dtm used for model training. Essentially, I need to populate the original dtm (as used for training) with new text. Example: "original …