Questions tagged [tfidf]

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2
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1answer
59 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 ...
0
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1answer
4k 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 ...
2
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3answers
73 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 - ...
0
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1answer
48 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
452 views

How to extract keywords from a list of URLs?

I have a bunch of URLs in a text file like- ...
1
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3answers
44 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 ...
1
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0answers
75 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
469 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 ...
3
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3answers
3k 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 ...
0
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1answer
64 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-...
2
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1answer
381 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 ...
1
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3answers
441 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 ...
0
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1answer
223 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): ...
4
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3answers
1k 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 ...
1
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1answer
35 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 ...
1
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1answer
28 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 ...
1
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1answer
1k 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 ...
3
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1answer
33 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 ...
1
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1answer
59 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 ...
1
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1answer
356 views

Does it make sense to use TF-IDF to extract most important tokens from a corpus?

I have a collection of documents and I'd like to extract the most important words and phrases from the entire corpus. My understanding of TF-IDF is that it is calculated per token per document, so ...
3
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1answer
258 views

TF-IDF vs TF for classification

Let's suppose that I have a dataset of 1000 documents. Each document is a restaurant review (so relatively short text) and it has labels {Negative, Indifferent, Positive}. Let's suppose that the ...
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0answers
158 views

SVM/Naive Bayesian text classification on multiple features

I was building a text classifier which takes into account certain features of the text and classifies them into two - "Yes" or "No". I have trimmed the text, removed stopwords and have applied TFIDF ...
0
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1answer
3k views

why does transform from tfidf vectorizer (sklearn) not work

I'm transforming a text in tf-idf from sklearn. I made the model: ...
0
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1answer
1k views

Will a Count vectorizer ever perform (slightly) better than tf-idf?

For the task of binary classification, I have a small data-set of a total 1000 texts (~590 positive and ~401 negative instances). With a training set of 800 and test set of 200, I get a (slightly) ...
1
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3answers
780 views

How do I use TF*IDF scores for my machine learning model?

I have applied TF*IDF on the 'Ad-topic line' column of my dataset. For every ad-topic line, I get the same output: Firstly, I am unable to make sense of the output. The TF*IDF values are mentioned to ...
1
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1answer
24 views

Use prediction as feature for a decision tree

I'm working at classifying documents according to their content. First I built a decision tree model that gives 90% of goods results. Then I tried a TFIDF/SVC approach which also gives 90% of good ...
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0answers
143 views

My naive (ha!) Gaussian Naive Bayes classifier is too slow

I am trying to build a film review classifier where I determine if a given review is positive or negative (w/ Python). I'm trying to avoid any other ML libraries so that I can better understand the ...
1
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2answers
205 views

How to use vectors produced by TF-IDF as an input for fuzzy c-means?

I have done text processing with TF-IDF method and as an output got a list of normalized vectors [0, 1] for each document. Such as below: ...
1
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1answer
109 views

Algorithm for document retrieval in QA system

I am working with question answering and machine reading comprehension system. I want to match questions and documents (around 100,000 docs) in database. I've used tf-idf but it accuracy is about 55% ...
1
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1answer
3k views

How to Combine tfidf with LSTM in keras?

I am classifying emails as spam or ham using LSTM and some of its modified form(by adding constitutional layer at the end). For converting documents into vectors I am using ...
1
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1answer
220 views

Assigning a new document to a cluster based on keywords extracted and tf-idf

I have about 40 clusters of documents defined by a combination of k-means clustering algorithm and hand curation. For example, some of the clusters given by k-means are too noisy so they have been ...
0
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2answers
65 views

Is this a good approach to classify tickets which contains description and logs?

I want to classify a dataset of support tickets which mostly contain text in the description field and sometimes server logs in a separate field. The log field is not always there but when it's ...
2
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0answers
323 views

Mixing Textual Data and Numerical Data (Neural Network)

I have 2 "nature" of data (more actually if I count images data) : Textual (that I treat with special tokenization and a TfIdfVectorizer) ~ 5000 features Non textual (like length of sentences, # of ...
4
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1answer
2k views

TF-IDF Features vs Embedding Layer

Have you guys tried to compare the performance of TF-IDF features* with a shallow neural network classifier vs a deep neural network models like an RNN that has an embedding layer with word embedding ...
3
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1answer
136 views

Predict the corresponding value in one column using a list of values found in another column

Please have a look at this link. This was a question I asked few months back and after some suggestions and exploring I was able to successfully use TFIDF along with MultinomialNB classifier to pretty ...
2
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1answer
530 views

Alternate of TF-IDF

I had used TF-IDF for text similarity but the results were not so good. I tried to implement google universal encoding (tensorflow hub). The results were satisfactory but not upto the mark. Is there ...
5
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1answer
4k views

Should I rescale tfidf features?

I have a dataset which contains both text and numeric features. I have encoded the text ones using the TfidfVectorizer from sklearn. I would now like to apply logistic regression to the resulting ...
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0answers
30 views

Predicting a new document [closed]

I have a document, (purchase agreement) of approx. 100 pages. This document is sent from buyer to seller several times, and each time there is a negotiation. Negotiation could be anything. For eg. ...
2
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1answer
3k views

Why TF-IDF is working with Sentiment Analysis?

Word2vec looks excellent to me as representation of corpus for sentiment analysis. It has relations between words etc. TF-IDF has only weight of the word how important it is. Results with sentiment ...
2
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0answers
450 views

DBSMOTE on Short Text Classification

I am trying to use DBSMOTE(Density-Based Synthetic Oversampling TEqnique) to on a data set of short text--tweets to be specific. This will be used to train a classifier model in a multiclass ...
5
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2answers
119 views

Online news classification

I am performing an online news classification. The idea is to recognize group of news of the same topic. My algorithm has these steps: 1) I go through a group of feeds from news sites and I recognize ...
14
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2answers
27k views

Word2Vec embeddings with TF-IDF

When you train the word2vec model (using for instance, gensim) you supply a list of words/sentences. But there does not seem to be a way to specify weights for the words calculated for instance using ...
0
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1answer
136 views

KDE on TF-IDF - sensitive bandwidth

I'm clustering texts based on TF-IDF features and DBSCAN (density based), and trying to rank points based on their 'belonging' to the cluster. Since my clustering is density based and my points can ...
1
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2answers
8k views

Sklearn tfidf vectorize returns different shape after fit_transform()

I'm new to ML and trying out basic samples using sklearn. I have achieved converting text (single dimension) to numbers using TF-IDF and got the predictions correct. Now I have a different use-case ...
1
vote
1answer
720 views

What affect will replacing words with bigrams have on TfIDF?

Say I have a corpus of text documents on which I have calculated each documents TfIDF vector. With this sparse matrix representation of the corpus, I can calculate similarities between documents by ...
1
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3answers
1k views

Idf values of English words

I'm working on keyword/phrase extraction from a single document. I started by doing term frequency analysis, but this returns words like "new" which aren't very helpful. So I want to penalize the ...
5
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3answers
5k views

Weighted sum of word vectors for document similarity

I have trained a word2vec model on a corpus of documents. I then compute the term frequency (the same Tf in TfIDF) of each word in each document, multiply each words Tf by its corresponding word ...
-1
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1answer
2k views

How to get relevancy score of a term with respect to text/document

I am working on the literature documents. I am able to identify important entities using NER and Ontologies. Now I will like to assign the relevance score to the identified entities with respect to ...
2
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0answers
651 views

TF-IDF Augmented Frequency vs Cosine Normalization

I am using TF-IDF for text classification and have been curious about the following two concepts. The augmented term frequency which is basically used for weighting in order to eliminate the bias ...
13
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3answers
15k views

Using TF-IDF with other features in scikit-learn

What is the best/correct way to combine text analysis with other features? For example, I have a dataset with some text but also other features/categories. scikit-learn's TF-IDF vectorizer transforms ...