Follow the given steps in order to solve the problem:1. Use MongoDB compass for ease.2. Open the "collection" in which you are facing a problem.3. Delete all the indexes that were showing errors through MongoDB compass.4. This should solve your problem.If the error persists then either delete the collection whose attributes were showing errors or delete an entire "collections" of that particular database and create new ones.You can also follow...
Tuesday, January 17, 2023
Wednesday, December 7, 2022
N-grams for text processing
N-gram is an N sequence of words. It can be a unigram (one word), bigram (sequence of two words), trigram (sequence of three words), and so on. It focuses on a sequence of words. Such a method is very useful in speech recognition and predicting input text. It helps us to predict the next words that could occur in a given sequence. Search engines also use the n-gram technique...
Tuesday, December 6, 2022
Word embedding techniques for text processing
It is difficult to perform analysis on the text so we use word embedding techniques to convert the texts into numerical representation. This is also called vectorization of the words. It is a representation technique for text in which words having same meaning are given similar representation. It helps to extract features from the text.i. Bag of Words (BoW)It is one...
Easy understanding of Confusion matrix with examples
Confusion matrix is an important metric to evaluate the performance of classifier. The performance of a classifier depends on their capability predict the class correctly against new or unseen data. It is one of the easiest metrics for finding the correctness and accuracy of the model. The confusion matrix in itself is not a performance measure but all the performance metrics...
Topic Modeling: Working of LSA (Latent Semantic Analysis) in simple terms
LSA (Latent Semantic Analysis) is another technique used for topic modeling. The main concept behind topic modeling is that the meaning behind any document is based on some latent variables so we use various topic modeling techniques to unravel those hidden variables i.e., topics so that we can make sense of the given document. LSA is mostly suitable for large sets of...
Topic Modeling: Working of LDA (Latent Dirichlet Allocation) in simple terms
Topic modeling is an unsupervised method used to perform text analysis. When we are given large sets of unlabeled documents, it is very difficult to get an insight into the discussions upon which the documents are based upon. Here comes the role of topic modeling. It helps to identify a number of hidden topics within a set of documents. Based on those identified topics,...
Bi-LSTM in simple words
In traditional neural networks, inputs and outputs were independent of each other. To predict next word in a sentence, it is difficult for such model to give correct output, as previous words are required to be remembered to predict the next word. For example, to predict the ending of a movie, it depends on how much one has already watched the movie and what contexts...
Topic Modeling
Topic modeling is an unsupervised technique as it is used to perform analysis on text data having no label attached to it. As the name suggests, it is used to discover a number of topics within the given sets of text like tweets, books, articles, and so on. Each topic consists of words where the order of the words does not matter. It performs automatic clustering of...
Challenges of Sentiment analysis
Lack of availability of enough domain-specific datasetMulti-class classification of the dataDifficult to extract the context of the post made by the usersHandling of neutral postsAnalyzing the posts of multiple langua...
Friday, March 18, 2022
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Saturday, June 26, 2021
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Saturday, November 28, 2020
Visualizing Decision tree in Python (with codes included)
List of libraries required to be installed (if not already installed). Here installation is done through Jupyter Notebook. For terminal use only "pip install library-name".#import sys#!{sys.executable} -m pip install numpy#!{sys.executable} -m pip install pandas#!{sys.executable} -m pip install sklearn#!{sys.executable} -m pip install hvplot#!{sys.executable} -m pip install...
Tuesday, November 10, 2020
Python code to extract Temporal Expression from a text (Using Regular Expression)
#Method 1Code:import reprint('Enter the text:')text = input()months='(Jan(?:uary)?|Feb(?:ruary)?|Mar(?:ch)?|Apr(?:il)?|May|Jun(?:e)?|Jul(?:y)?|Aug(?:ust)?|Sep(?:tember)?|Oct(?:ober)?|(Nov|Dec)(?:ember)?)'re1=r'\w?((mor|eve)(?:ning)|after(?:noon)?|(mid)?night|today|tomorrow|(yester|every)(?:day))' re2=r'\d?\w*(ago|after|before|now)'re3=r'((\d{1,2}(st|nd|rd|th)\s?)?(%s\s?)(\d{1,2})?)' % monthsre4=r'\d{1,2}\s?[a|p]m're5=r'(\d{1,2}(:\d{2})?\s?((hour|minute|second|hr|min|sec)(?:s)?))'re6=r'(\d{1,2}/\d{1,2}/\d{4})|(\d{4}/\d{1,2}/\d{1,2})'re7=r'(([0-1]?[0-9]|2[0-3]):[0-5][0-9])'re8=r'\d{4}'relist=...
