Questions tagged [tfidf]

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29 views

Ordering of standardization, pca, and/or tfidf for neural network

I have 60k rows of text data. I have tokenized it into 55k columns. I am using a neural network to classify the data but have some questions about how to order my preprocessing steps. I have too much ...
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1answer
20 views

Is it good practice to remove the numeric values from the text data during preprocessing?

Im doing preprocessing on a text dataset. I have certain numerics in it like: date(1st July) year(2019) tentative values (3-5 years/ 10+ advantages). unique values (room no 31/ user rank 45) ...
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3answers
33 views

How to process the hyphenated english words for any nlp problem?

Im doing preprocessing on english text dataset. I encounter hyphenated words like 'well-known'. Will it be useful if I remove the hyphen as special character and treat it as a single word 'wellknown' ...
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20 views

Does it make sense to use TF-IDF matrix as Embedding layer weights in Keras

Keras Embedding layer accept weights pre-trained from GloVe for example. A classic NN should look like this: ...
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1answer
25 views

TF-IDF for Topic Modeling

Can TF-IDF be used a sole method for Topic Modeling ? (I know there are better methods like LDA , LSA etc) I just want to understand if TF-IDF alone can help us in Topic modeling . If yes , can ...
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11 views

Manually tune tf-idf features in document classification

I am working on a multi-label document classification task with a very small data set (180 labeled documents) and a fairly large number of labels (20). I found that - ignoring label correlations and ...
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17 views

TF-IDF Transform duplicating data

I'm working on a Sentiment Analysis task using TF-IDF to build my features and SVC as the classifier. My goal is to make my model to classify the sentiment of all my dataset. I already designed my ...
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1answer
20 views

Word representation that gives more weight to terms frequent in corpus?

The tf-idf discounts the words that appear in a lot of documents in the corpus. I am constructing an anomaly detection text classification algorithm that is trained only on valid documents. Later I ...
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1answer
19 views

How does TF-IDF classify a document based on “Score” alloted to each word

I understand how TF-IDF "score" is calculated for each word in a document, but I do not get how can it be used to classify a test document. For example, if the word "Mobile" occurs ...
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1answer
47 views

How to handle unseen labels in test data?

I get something like TF-IDF of training corpus in python with (something like) TfidfVectorizer. In test data some features (here are the words of test corpus, every word is a feature) are not seen in ...
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25 views

TFIDF and TFIDF weighted W2V with Multinomial Naive Bayes?

Although the tfidf vectors don't really follow Multinomial Distribution, yet MultinomialNB works fairly well, why is it so? Also would weighted tfidf w2v work the same way or should I use GaussianNB ...
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20 views

How best to embed large and noisy documents

I have a large corpus of documents (web pages) collected from various sites of around 10k-30k chars each, I am processing them to extract relevant text as much as possible, but they are never perfect. ...
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1answer
26 views

How come same cluster category be separated?

I have these 200 vectors which were clustered using K-means clustering based on keywords weight similarity that was given by TF-IDF (Term Frequency - Inverse Document Frequency). The vectors were ...
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2answers
28 views

Is it accurate to say that “K-means clustering the vectors based on keywords weight similarity”?

Long story short, I have 200 vectors as a result of TF-IDF (Term Frequency - Inverse Document Frequency) on thousands of keywords in hundreds of vectors. The total number of unique keywords that I got ...
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27 views

How to properly vectorise when I have several text features?

I am having several features as text and I'm using them once for a classification problem and then later for a regression problem. The textual data features themselves aren't categorical. Because as ...
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18 views

SKLearn NearestCentroidClassifier score with predict_proba

I'm using the NearestCentroidClassifier combined with TF-IDF for classification of documents. The are linked to a growing number of document groups. I've set sklearns TfIdfVectorizer and the ...
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1answer
49 views

What is the formula and log base for idf?

To calculate tf-idf, we do: tf*idf tf=number of times word occurs in document What is formula for idf and log base: Log(number of documents/number of documents containing the word) Log((1+number ...
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10 views

Using kernel estimation to find similarity/difference between two feature sets for binary classification

I am trying to train a binary classifier using word vectors. I have the tfidf vectors for each sentence in my training set. Before applying binary classification algorithms, I just want to check ...
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18 views

Topic alignment / topic modelling

What is the most efficient method for detecting whether the article is mostly about a specific topic, but without lots of data for training? My task is to determine how much a document is e.g. about ...
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19 views

Training a cosine similarity matrix for similar text recommendation

I'm working on similar movie names recommender system. I have a dataset of only movie_titles that I converted into matrix using tfidf and then computed the cosine ...
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12 views

Should I create a tfidf on a subset of a data set or use the whole corpus?

My goal in this project is to see if businesses on a list are currently customers within my organization. One piece of this involves producing a similarity score using cosine similarity on the names ...
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0answers
23 views

Semi-Supervised Learning using NLP

I am working on a drug reaction problem in which I need to extract tweets and label the tweets (binary-reaction due to drug or not). But since I don't have domain knowledge, and clustering would also ...
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1answer
19 views

Text vectorizer that capture feature offset in the text?

I'm using sklearn Tfifdfvectorizer to extract feature from text towards text classification. I believe the information I need tends to be in the beginning of the document, so I would like to somehow ...
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16 views

Calculating only document frequency or only term frequency from TF-IDF

I have a large corpus of documents (multiple sentences per document), and am trying to find words that appear in some majority of documents (to then filter them out of a future analysis). Since I'm ...
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1answer
17 views

Solution for TF-IDF Vectorization in Angular project?

While making an Angular project to use my text-classification model on unseen data, i struggle in finding a way how to transform text to TFIDF features. Anyone faced same issue? Maybe a solution on ...
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0answers
584 views

ValueError: Found input variables with inconsistent numbers of samples: [2, 44] [closed]

Have a piece of code where i am cleaning the text from the 'Description' column and storing it as "cleaned" Then i create a ML model using the above as one of my features. ...
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1answer
1k views

Word2Vec and Tf-idf how to combine them

I'm currently working in text mining ptoject I'd like to know once I'm on vectorisation. With method is better. Is it Word2Vec or ...
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1answer
111 views

Using TF-IDF for feature extraction in Sentiment Analysis

I am working on sentiment analysis for twitter data, for which I have used Vader to get an approximation of sentiment for a tweet. Along with, I have used TF-IDF for feature extraction. These feature ...
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1answer
84 views

Unable to save the TF-IDF vectorizer

I'm workig on multi-label classification problem. I'm facing issue while saving the TF-IDf verctorizer and as well as model using both pickle and joblib packages. Below is the code: ...
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1answer
51 views

Dealing with low-information centroids using Nearest Centroid Classifier and bag of words method

I am currently working on a problem where we have projects and e-mails that belong to a single project each. My goal is to create a recommendation system for incoming e-mails which presents the ...
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1answer
1k views

How to choose the best parameter values for TfidfVectorizer in sklearn library?

Recently, I used TfidfVectorizer in scikit-learn library to calculate a matrix of TF-IDF features. However, I do not know how to set some parameters such as ...
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3answers
72 views

How to approach TF-IDf based analysis?

Problem statement : We have documents with list of words in them. Overall these documents are classified into 2 group (say, good quality vs bad) docs - ...
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1answer
35 views

CV(Curriculum vitae) Recommendation System guidance

I am building a recommender system which matches people's CV with a vacancy. So far, I used TF-IDF & Cosine Similarity to get a matching score between a vacancy and a candidate's CV. I want to ...
2
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1answer
65 views

How to extract keywords from a list of URLs?

I have a bunch of URLs in a text file like- ...
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3answers
34 views

Vectorize One line text data

How to vectorize one-line text data? I have used tf-idf including bigrams and trigrams but I am not able to get good results. I have purchase order descriptions which are one-liners and I need to ...
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0answers
32 views

Setting a threshold for tfidf

Let's say I have streaming textual data coming in. I run named entity recognition software to capture the entities, then I score them using tf-idf. Tf-idf scores are unbounded positively. How can I ...
4
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1answer
296 views

TS-SS and Cosine similarity among text documents using TF-IDF in Python

A common way of calculating the cosine similarity between text based documents is to calculate tf-idf and then calculating the linear kernel of the tf-idf matrix. TF-IDF matrix is calculated using ...
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3answers
1k views

Are stopwords helpful when using tf-idf features for document classification?

I have documents of pure natural language text. Those documents are rather short; e.g. 20 - 200 words. I want to classify them. A typical representation is a bag of words (BoW). The drawback of BoW ...
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1answer
42 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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1answer
303 views

Why is the result of CountVectorizer * TfidfVectorizer.idf_ different from TfidfVectorizer.fit_transform()?

I have a dataframe: df = pd.DataFrame({'docs': ['gamma alfa beta beta epsilon', 'beta gamma eta',], 'labels': ['alfa alfa beta', 'gamma fi']}) I use count ...
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2answers
270 views

How to implement HashingVectorizer in multinomial naive bayes algorithim

I had used TfidfVectorizer and passed it through MultinomialNB for document classification, It was working fine. But now I need to pass a huge set of documents for ex above 1 Lakh and when I am ...
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1answer
60 views

TF-IDF: How to handle terms not part of the corpus

I'm working on a ML.Net based feature to extract keywords from a document corpus using TD-IDF. Given this test corpus (one document per line): ...
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1answer
290 views

Word Embeddings with TFIDF vectorizer

I am a beginner in machine learning. I have a large corpus of texts, divided into thematic groups. I would like to get idf values for the whole corpus, and then apply it on each group before ...
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3answers
433 views

TFIDF for very short sentences

I'm trying to build a regression model, in which one of the features contains text data. I was thinking in using scikit-learn's ...
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1answer
30 views

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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1answer
26 views

How do we decide on the classification algorithm to use with huge training size?

I am solving a questions binary classification problem and the training size for this is huge(291 billion). The data has bloated because of using tfidfvectorizerfor ...
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1answer
541 views

How to combine nlp and numeric data for a linear regression problem

I'm very new to data science (this is my hello world project), and I have a data set made up of a combination of review text and numerical data such as number of tables. There is also a column for ...
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1answer
31 views

Predicting probability for each tag given already chosen tags

I have a set of tags (~10'000, will be extended over time) presented to a user. After he has selected 3 or more tags, I want to predict for each remaining tag what the chances are that the user will ...
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1answer
47 views

Checking TF-IDF Results

I am working with TF-IDF and cosine similarity to do document comparisons and given a document, which document in the data is the most similar. However, sometimes it returns a high similarity between ...