Questions tagged [tf-idf]

In information retrieval, tf–idf, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.

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How to add bigram stopwords when text clustering with tf-idf and k-means [closed]

Summary: I'm doing text clustering with using tf-idf and k-means clustering. I have followed this article to do so. While I haven't fully understood what's going on with the clustering side, the ...
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PCA on TF-IDF matrix

I want to perform PCA on TF-IDF matrix, but I am not sure, should I center this matrix first or not? And should I do scaling or just centering?
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Parameter of CountVectorizer(min_df=10, ngram_range=10,max_features=5000)

vectorizer = CountVectorizer(min_df=10, ngram_range=10,max_features=5000) I understand what these parameters are and I know what it does, but I still have few question on these parameters (i.e. ...
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Why does TF-IDF use the count of the most common word in the document instead of total count of all words in the document?

From what I understand, the "term frequency" ("TF" in TF-IDF) is calculated from the number of times a certain term occurs in a document vs the most common term in a document: ...
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How does adding/omitting lemmatization affect TF-IDF?

Lemmatization has some obvious benefits in TF-IDF, e.g. it decreases the vocabulary size. What are some other advantages, and what are some disadvantages to lemmatizing in the context of TF-IDF?
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How to calculate tf-idf for a single term

I am following the tf-idf method described in this paper: Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte ...
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21 views

Alternative to tf-idf with smaller penalty on previous usage

I am using tf-idf to find words that are particularly important to individual documents. This works pretty well for my purposes. However, one area where I feel like it isn't great is how harshly words ...
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Unsupervised anomaly and outlier detection of database queries

I'm monitoring database queries coming from multiple different applications spread across numerous systems and I'd like to find both anomalous queries as well as outliers in a completely unsupervised ...
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118 views

TF-IDF versus TF for Cosine Similarity

My task is to examine emails and determine the cosine similarity between pairs of emails to see which ones are the same or almost the same. I was thinking of using the TF-IDF technique, but am not ...
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Can labels in a dataset be a vector (or just anything non-primary)?

I am building a recommender system for coding interview questions and want to test this by using what the actual system has recommended. The similar_questions below ...
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How to validate results for TF-IDF

I am currently using TF-IDF and cosine similarity for document comparison. I am getting some results that, on face value don't make sense, more specifically when looking through the document I don't ...
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What's the effect of using TF-IDF encoded instead of one-hot encoded categorical data as input to a neural network?

As input into a simple neural network multi-class classifier, I am considering using a variation of the standard one-hot sparse matrix to represent categorical variables. Instead of each element ...
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Use raw count, Term Frequency or Log Normalized with Cosine Similarity?

For the task of finding duplicate documents within a corpus of documents I have been using a TF-IDF vectorizer (from sklearn in Python) combined with the Cosine Similarity between the document vectors....
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Applying TF/IDF to non-text data?

I have a classification problem in which I am supposed to predict the end state of an object based on a set of events it experiences. There are about one thousand possible events and each object is ...
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30 views

Improve the accuracy of semantic text matching

I have a corpus of ~200K sentences of variable length, the median length is 16 words. My goal is for a given sentence to find other sentences with a similar meaning. I tried several approaches: using ...
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Why does 4-gram work better than trigram or bigram or unigram in my experiments?

In a binary classification task, I used Logistic regression, decision tree and Adaboost with decision tree (max_depth=1). For each of the machine learning task, I used GridSearchCV to choose the ...
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677 views

Determining epsilon for DBSCAN

I'm using the method described in this paper for determining the optimal epsilon value for DBSCAN clustering in which a plot of the nearest neighbors is used: However, the plots in the paper and ...
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Kolmogorov–Smirnov test on text data

The Kolmogorov–Smirnov test a very efficient way to determine if two samples are significantly different from each other or whether the CDF between two different samples fit each other. This can be ...
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Did my text data come from two distinct distributions?

I have labeled text data from two different classes. I have calculated tfidf feature representation of all the sentences in question. I have a huge matrix where rows are sentences and columns are ...
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81 views

High Precision and low recall score for TF-IDF when using KNN algorithm

I have twitter data which is labelled with the sentiment(Postive, Negative, Neutral) and I have evaluated the performance of Tf-Idf and Doc2Vec feature extractor using the KNN algorithm and logistic ...
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62 views

Correct algorithm for string classification

I have a long list of DNA strings (of equal length) made of 4 letters (A,T,G,C). I want to do a binary classification on the strings. I have two basic quetsions: I have a lot of duplicate strings ...
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Clustering algorithms for multiple features that are arrays/lists

my issue is that I want to cluster data from a specific context. The dataset was 3 different texts that cannot be concatenated. Now I have the following "data-object" in Python: It is an array with ...
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What is the maximum number of features in Logistic Regression Problem

I was doing Text classification(binary) hosted on kaggle with approx 1.3 millions observations. My approach is to use Logistic Regression after computing the TF-IDF matrix with n-grams = 1:3. With ...
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Negative values in word vectorizations

I am currently in the middle of reading Applied Text Analysis with Python by Bengfort, Bilbro, and Ojeda, and encountered a sentence that I've struggled to wrap my head around. In the section ...
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Which is the best strategy for clustering a glossary of terms by exploiting their definitions

I have a glossary (dictionary) of terms together with short definitions (1 to 20 words). I want to cluster these terms by their application domains by using their definitions; for example, clustering ...
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116 views

Applying Label From Supervised Learning to Unlabeled Data- Text Classification

I am wondering if anyone has code to following: 1) Apply labels from a previous text classification dataset like this type of data (https://colab.research.google.com/drive/...
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Text Similarity - Cosine - Control. Suggestion to another / better method?

I would like to ask you, if anybody could check my code, because it was behaving weird - not working, giving me errors to suddenly working without changing anything - the code will be at the bottom. ...
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163 views

TF-IDF String to Vector Weka bias

For example, let's say I have a text dataset like: "words text etc",label "words text etc",label "words text etc",label If I use Weka's String to Word ...
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665 views

How to combine two tfidf sparse vectors

Say that I have two document collections that I have created a tf-idf sparse vector for each one using TfidfVectorizer. How could I combine those two vectors into one that would resemble the tfidf of ...
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658 views

Tf-idf for text classification: On what should IDF be calculated?

The TF-IDF value of a word specifies how important a word for each document is. My setting is any text classification where one has multiple documents of with different classes: Let's take a lot of ...
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164 views

How to compute k-means on provided inverted index using tf-idf

I built an inverted index to represent the following sample documents: ...
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287 views

Practically speaking, is the TF-IDF threshold universal across different corpus?

I would like to know the practical threshold of the TF-IDF (just like the practical p-value cutoff of 0.1 or 0.05 in hypothesis tests). I tried to look at it in some previous post, and some people ...
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Delta TF-IDF right choice for multi classification problem

In the paper of Martineau & Finin they describe their new approach with Delta TF-IDF . Instead of measuring how rare features are in the document, they weight these values by how biased they are ...
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Why is the size of fitted truncated svd model is so big?

I have a dataset with tfidf matrix of shape (200000, 565000). I am fitting truncated svd of 500 dimensions from sklearn onto it and pickling the resulting svd object for later use. The pickle file is ...
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503 views

How to improve performance for LDA?

I am running LDA on health-related data. Specifically I have ~500 documents that contain interviews that last around 5-7 pages. Other than that, I cannot really go into the details of the data due to ...
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TF-IDF vs just TF in text classification [duplicate]

It's common to see people using a tf-idf representation of words for text classification, but I don't understand why not just use tf. Say $tf(t,d)$ is the $tf$ of term $t$ in a document $d$ and $idf(...
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346 views

Text classification using tf idf

I am developing a text classification model. At this moment I have to classify some documents in six different classes. I am using a simple approach as a starting point based on random forests over a ...
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74 views

Using k fold cross validation gives lower results than without using it

I have implemented text classification in the sentence level by following through this tutorial. I have used tf-idf and NB & SVM as shown in the tutorial. The code is working fine with my dataset. ...
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109 views

How to compute tf-idf for sequential k means?

I am trying to run the sequential K means algorithm as described here on a corpus using tf-idf as a vectorized representation of my documents. I do this because I don't have access to all of my ...
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251 views

Use tf-idf for QnA

I have a corpora of queries and answers where the queries are the title of different documents and the answers are a short description of them. E.g: Title: African Civilian Deaths Description:...
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491 views

Accuracy increases on decreasing the percentage of training data with stable precision, recall and F-score

I am currently working on a classification problem using tf-idf and Naive Bayes for two classes A and B. I have randomly shuffle the dataset before implementation, and I was experimenting with the ...
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TF-IDF on a sub-corpus

I'm analyzing newspaper articles, some are 2 or 3 pages long, some a few lines long. My Corpus is made of a few thousands of dated articles spanning over a few ...
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How does correlation work in text mining?

When we are done in pre-processing the text (removing stop words, stemming, lower case) then creating a term document that has TF-IDF as weight. How can I decide what correlation test to do between ...
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What does word embedding weighted by tf-idf mean?

The paper that I am reading explains about how it implemented the feature vector used for a twitter sentiment classification task. The first is a simple combination, where each tweet is represented ...
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How TF is calculated?

Im really confused about how TF is calculated. I understand that TF = count of term x/ total # of terms For example: ...
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Understanding and interpreting the output of Spark's TF-IDF implementation

I am currently trying to understand what the example code provided as part of Spark's TF-IDF implementation is doing. Given the example code block (taken from Spark's Github repository) ...
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How to deal with TFIDF for test classification?

I am working on a project where I have to classify texts. For that, I am dividing my data into training and test data. In order to train my classifier(to be determined later), I am planning to ...
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1answer
464 views

Finding the top words in a text

(I have no actual background in data science or statistics, so please easy with the math and concept names). I have a text file (lets say 40k words), and I want to find the top 10 words with the ...
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Does triplet loss help for document similarity search?

I build CNN network over documents with triplet loss. And compare documents with cosine similarity. It does really find similar docs and catches interesting dependencies. But simple tfidf model does ...
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How is the .similarity method in SpaCy computed?

Not Sure if this is the right stack site, but here goes. How does the .similiarity method work? Wow spaCy is great! Its tfidf model could be easier, but w2v with only one line of code?! In his 10 ...