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Questions tagged [text-mining]

Refers to a subset of data mining concerned with extracting information from data in the form of text by recognizing patterns. The goal of text mining is often to classify a given document into one of a number of categories in an automatic way, and to improve this performance dynamically, making it an example of machine learning. One example of this type of text mining are spam filters used for email.

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Common text data sets in form of panel data [migrated]

I want to test machine learning tasks on time-divided textual data set. For this purpose, I want to use a common text data set which is already validated and "good" for use. I already found a Web of ...
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How can you make use of Json format data?

I want to obtain data from https://petition.parliament.uk/petitions/250967 but the data format is Json. I am new to data mining and I would like to know if there is a way to convert this data into an ...
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Is there any pre-trained model for identifying text Context and Intent? [closed]

How can we identify the context of Text and how can we categories text in Medical related or technical related text?
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Unsupervised learning, Python, text clustering

I want to do unsupervised learning. As I understand with such learning we don't know the clusters before, right? I read about k-means alghoritm, followed mainly these two articles: The first article. ...
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20 views

PDF/Text to csv table

I have very little python experience but I have been wanting to get into data science and thought I would start with pdf/text mining. Because it's something I need right now anyway. I have a list of ...
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Looking for recommendations on auto-tagging approaches

Basically looking for approaches to solve the stackoverflow question tagging problem. I have seen a few papers already but in case I have missed something - asking here too. For anyone interested, ...
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how to analyse open ended responses

I have a dataset in which 5 fields are open-ended responses. I need to find interesting insights from this data. My approach is: create a word2vec and then form different clusters and make these ...
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Could I use the harmonic mean method to determine k number of topics when applying Latent Dirichlet Allocation using text2vec?

I am using text2vec to apply LDA on 230k docs reduced to 800 terms aprox. Is it okay to use the harmonic mean to approximate the marginal likelihood in order to mention the best topic number when that ...
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Latent Dirichlet Allocation in R, topicmodels using VEM algorithm or Gibbs Sampling mixing tm and topicmodels library or WarpLDA from text2vec?

If I am trying to classify 230k text abstracts, which option would be better and more precise when aplying LDA?
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Classifying dates in sentences

Let's say I have a sentences that goes like this: Hi, how are the kids. I will be going to Los Angeles next Friday and will come back the following Monday. If the date today is October 16 (...
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21 views

Information Extraction/Semantic Search for long, unstructured documents

I am stuck with a particular task of information extraction. I have a few hundred, long (5-35 pages) pdf, doc and docx project documents from which I seek to extract specific information and store ...
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How to approach the topical text categorization of a small collection of short texts?

I have a set of 200 very short documents, between 1 and 20 words each. One of my colleague would like to classify each of these documents in three predefined topics (let's call them "A", "B", and "C"...
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Collecting structured data from HTML source code: A generalized way

I am working on a task to build a generic function to extract some specific fields from HTML source code. The fields we want are such as product title, price, quantity and shipment The generic ...
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25 views

Finding cosine similarity score

I have a dataframe that looks like this: sentence intent hi greeting hello greeting buy this buy whats up conversation . . What I'd like ...
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1answer
23 views

Why is TF IDF output lognormal?

I ran a TF IDF algorithm and the result of predicted similarities using cosine similarity is a log-normal distribution. Is this a feature of the algorithm (e.g., all logit probabilities are log-...
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POS extraction using CoreNLP

I have a corpus of windows related documents, for which I need to extract nouns and verbs. However, it is required that I keep certain windows specific words such as "inline hooking", "instruction ...
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1answer
21 views

Can you suggest a better to organise the data to generate frequent itemsets?

I have a data of a bag of words in a document. The data has 3 coulumns : document number, word number, count of the word in the number. I am supposed to generate frequent itemsets of particular size. ...
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appending file text into list with file name in python

Here 20_newsgroups is a folder which contain 20 folder and each folder contain some file , i just want to make datasets containind words and file name its gives Error ...
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1answer
18 views

Infer family type, size from reviews

I have a bunch of reviews: ...
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20 views

Probabilistic model of selecting subsets of words from documents?

Is there an existing probabilistic model that deals with the selection of subsets of words from a corpus of documents? Imagine a stack of documents where a subset of the words in each document has ...
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how to calculate coherence score in topic model

I am trying to calculate coherence score in topic modeling. I am following this Github link So there I need to use the preprocessed wiki and news. I got 3 questions: if the domain that I have ...
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Can we treat sentiment score of the review text as rating of the product?

I have a review text of the different products, And I need the rating of the product. So can we use sentiment score as the rating of the product.
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11 views

Is it possible to build an intelligent lead classifier with just a few training units

I want to build a lead classifier for my Master Thesis and wanted to ask for an assessment of feasibility. Here are the key points: (1) We have 15 customers and about 100 opportunities of which we ...
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NLP to detect duplicates for very technical language

I have the following scenario, to detect duplicate products based on the description fields. The Description Field contains product technical name, dimensions, characteristics. My model needs to ...
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Sentiment Analysis for Q&A based reviews

I'm a self-learning ML enthusiast and I recently started learning NLP and performing Sentiment Analysis on imdb, yelp, amazon datasets(using Python). I came across a dataset where the reviews were in ...
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1answer
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Given two large corpora of text from different sources, is there an accepted way to get differences in vocabulary (n-grams) between them?

Given two large corpora of text from different sources, is there an accepted way to get differences in vocabulary (n-grams) between them? That is, to get results which say that, for example, the ...
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27 views

Which are the appropriate prameters for lda modeling?

I try to implement in R test for appropriate metrics for lda. Here the way I try to use LDA ...
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What options do I have for measuring similarities by using vectors generated from texts?

I have a data set which contains vectors generated from subtitles, I want to measure the similarity between each pair of the observation. Now I have tried L1, L2, cosine similarity and Mahalanobis ...
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Text extraction / mining from specific templates (ML)

believe me if I say that I have read basically all the threads in this website regarding this subject. A lot of them have a similar title, but the problem is somehow different. Small context: just ...
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1answer
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How to segment old digitized newspapers into articles

I'm working on a large corpus of french daily newspapers from the 19th century that have been digitized and where the data are in the form of raw OCR text files (one text file per day). In terms of ...
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106 views

Sentiment Analysis of News Headlines

I'm trying to do sentiment analysis of News Headlines about a particular subject mentioned in it. Initially, I used TextBlob library for sentiment analysis to ...
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Clustering text documents from multiple sources

Let's say I have a set of text documents. Half of the documents are concise social media posts containing a lot of shorthand, and the other half are long news articles. Also, half of the documents ...
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How to compare different similarity measurements in text clustering?

I have a dataset which contains vectors generated from subtitles (each column represents a genre, each row is a movie name), my purpose is to find the most similar movie titles, I want to use ...
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pros and cons of lexical vs machine learning methods for text mining

I wanted to know what are the pros and cons are of using lexical methods and machine learning methods for classifying texts based topic. I have used a simple method of mining documents related to a ...
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1answer
50 views

Data Entry Automation with ML

I am working on a data entry task with approximately 6000 entries to go over. The source comes in the form of a string and can look something like this: Air Canada B737 FFS From this I can ...
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27 views

Grouping paragraphs of text by type

I'm trying to parse some text, and extract data from it. Typical NLP problem. However the text contains different sections, and I know that the keywords of interest are in specific sections, but all ...
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27 views

How to classify product by specific category without machine learning?

I am working on a product classification problem which I have to identify product category. Say for the category, there are 5 levels (Big / medium / small / detail / double detail) 5 million ...
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Clustering of words based on ability to predict variation in other variables

I have a data set of about 1000 observations like so: ...
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19 views

Class Size Imbalance for LDA or any other Content based analysis

I am running some content analysis studies on my dataset which has two different classes, and each class has a respective list of the document I am analyzing. I compare the LDA topic model inference ...
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1answer
27 views

Technical term for using regular expressions to classify text?

Background I'm helping a researcher programmatically classify ~123,000 US Government court case files stored in plaintext. He wants to classify the claims as either having been "approved", "denied", ...
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9 views

Classifying Short Texts with Spatial Features

I have a dataset of short texts (like tweets) in addition there's some geographical data attached to each tweet - coordinates, whether it was made on the road, street, outside or in the building, ...
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1answer
144 views

DBSCAN clustering on document [updated]?

I am new in topic modeling and text clustering domain and I am trying to learn more. I would like to use the DBSCAN to cluster the text data. There are many posts and sources on how to implement the ...
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1answer
35 views

Build text complexity model based on complex examples

I try to build the user specific model which predicts whether arbitrary English text is complex for particular user or not. Having the complex and easy text samples allows to build such model but what ...
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How to handle variables with text and number?

This is a post with two related questions in one. The first question is: What is the correct procedure when I have variables with different kind of information? Imagine you have a column which has ...
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1answer
66 views

Find specific topics with topic modelling

I am looking for a way to classifiy text automatically by specific topics, i don´t have labeled data. Is this a possible/usual method of achieving this? If not, what would be better? Topic Modelling ...
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1answer
38 views

Emotional tension score in sentences

I am beginner in natural language processing and my goal is to find a way to score sentences based on their emotional tension. More specifically, I would like to know to what degree a sentence ...
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14 views

How to interpret the transition and feature weights in a Conditional Random Field model?

Conditional Random Fields model have been a popular method for Named Entity Recognition as it accounts for statistical dependencies between entities and can include observed features that can aid with ...
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Targeted information extraction / focused extractive summarization

I have a large collection of project manuals, each with a large number of pages. Each manual contains some form of summary paragraphs, although these are not necessarily similar in structure or format ...
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1answer
172 views

Which libraries in Python are there in NLP to tokenize the Hindi sentence?

For English language there are libraries like NLTK, CoreNLP which are used for Text Normalization, Word Tokenization and Detokenization, Sentence Splitting etc. Like English, is there any library to ...
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Assigning tags to posts using predefined set of tags

I want to tag the text of a post with a predefined set of tags. A post could have multiple tags such as health, addiction, etc. I want to recommend up to $5$ tags. Total of $60$ tags is present. ...