Name		Name	Last commit message	Last commit date
Latest commit History 89 Commits
1-bag-of-words.ipynb		1-bag-of-words.ipynb
10-processing-the-bible-corpus.ipynb		10-processing-the-bible-corpus.ipynb
10. Topic Models using LDA.ipynb		10. Topic Models using LDA.ipynb
11. Continuous Bag of Words, Word2Vec Model.ipynb		11. Continuous Bag of Words, Word2Vec Model.ipynb
12. Implementing a word2vec model using a skip-gram neural network architecture.ipynb		12. Implementing a word2vec model using a skip-gram neural network architecture.ipynb
13. Robust Word2Vec Models with Gensim.ipynb		13. Robust Word2Vec Models with Gensim.ipynb
14. Applying Word2Vec Features for Machine Learning Tasks.ipynb		14. Applying Word2Vec Features for Machine Learning Tasks.ipynb
15. The Glove Model for Document Clustering.ipynb		15. The Glove Model for Document Clustering.ipynb
16. Applying FastText Features to Machine Learning Tasks		16. Applying FastText Features to Machine Learning Tasks
17. Text Classification.ipynb		17. Text Classification.ipynb
18. Key-Phrase Extraction Based on Collocations using n-gram,NLTK's pmi and raw frequency.ipynb		18. Key-Phrase Extraction Based on Collocations using n-gram,NLTK's pmi and raw frequency.ipynb
19. Weighted tag-based phrase extraction.ipynb		19. Weighted tag-based phrase extraction.ipynb
2-bi-gram-based-feature-for-bag-of-n-gram-model.ipynb		2-bi-gram-based-feature-for-bag-of-n-gram-model.ipynb
20. Topic Models with Latent Semantic Indexing with Gensim.ipynb		20. Topic Models with Latent Semantic Indexing with Gensim.ipynb
21. Topic Models with Latent Dirichlet Allocation with Gensim.ipynb		21. Topic Models with Latent Dirichlet Allocation with Gensim.ipynb
22. Topic Models with MALLET.ipynb		22. Topic Models with MALLET.ipynb
23. Topic Models with Latent Semantic Indexing with Scikit-Learn.ipynb		23. Topic Models with Latent Semantic Indexing with Scikit-Learn.ipynb
24. Topic Models with Latent Dirichlet Allocation with Scikit-Learn.ipynb		24. Topic Models with Latent Dirichlet Allocation with Scikit-Learn.ipynb
25. Topic Modeling with Non-Negative Matrix Factorization with Scikit-Learn.ipynb		25. Topic Modeling with Non-Negative Matrix Factorization with Scikit-Learn.ipynb
26. Visualizing Topic Models.ipynb		26. Visualizing Topic Models.ipynb
27. Automated Document Summarization, extraction-based document summarization..ipynb		27. Automated Document Summarization, extraction-based document summarization..ipynb
28_Hamming_Distance.ipynb		28_Hamming_Distance.ipynb
29_Manhatten_Distance.ipynb		29_Manhatten_Distance.ipynb
3-tfidftransformer .ipynb		3-tfidftransformer .ipynb
30_Euclidean_Distance.ipynb		30_Euclidean_Distance.ipynb
31_Levenshtein_Edit_Distance.ipynb		31_Levenshtein_Edit_Distance.ipynb
32_Cosine_Distance_and_Similarity(Analyzing_Term_Similairty).ipynb		32_Cosine_Distance_and_Similarity(Analyzing_Term_Similairty).ipynb
33. recommender system with document similarity wiht Cosine Similarity.ipynb		33. recommender system with document similarity wiht Cosine Similarity.ipynb
34. recommender system with document similarity wiht Okapi BM25.ipynb		34. recommender system with document similarity wiht Okapi BM25.ipynb
35. Clustering Movies with K-Means.ipynb		35. Clustering Movies with K-Means.ipynb
36. Clustering Movies with Affinity Propagation.ipynb		36. Clustering Movies with Affinity Propagation.ipynb
37. Clustering Movies with Ward's Agglomerative Hierarchical Clustering.ipynb		37. Clustering Movies with Ward's Agglomerative Hierarchical Clustering.ipynb
38. Analyzing Lexical Semantic Relationships.ipynb		38. Analyzing Lexical Semantic Relationships.ipynb
39. Word Sense Disambiguation Lesk algorithm .ipynb		39. Word Sense Disambiguation Lesk algorithm .ipynb
4-constructing-count-based -count-based bag-of-words-from-scratch.ipynb		4-constructing-count-based -count-based bag-of-words-from-scratch.ipynb
40. Named Entity Recognition using Spacy.ipynb		40. Named Entity Recognition using Spacy.ipynb
41. Named Entity Recognition using base Stanford NLP NER tagger.ipynb		41. Named Entity Recognition using base Stanford NLP NER tagger.ipynb
42. Building an NER Tagger from Scratch Using Conditional Random Fields.ipynb		42. Building an NER Tagger from Scratch Using Conditional Random Fields.ipynb
43. Building an End-to-End NER Tagger With our Trained NER Model.ipynb		43. Building an End-to-End NER Tagger With our Trained NER Model.ipynb
44. Analyzing Semantic Representations, Propositional Logic.ipynb		44. Analyzing Semantic Representations, Propositional Logic.ipynb
45. Analyzing Semantic Representations, First Order Logic.ipynb		45. Analyzing Semantic Representations, First Order Logic.ipynb
46. Unsupervised Lexicon-Based Models TextBlob lexicon based model.txt.ipynb		46. Unsupervised Lexicon-Based Models TextBlob lexicon based model.txt.ipynb
47. Unsupervised Lexicon-Based Models AFINN lexicon based model.txt.ipynb		47. Unsupervised Lexicon-Based Models AFINN lexicon based model.txt.ipynb
48. Unsupervised Lexicon-Based Models SentiWordNet lexicon based model.txt.ipynb		48. Unsupervised Lexicon-Based Models SentiWordNet lexicon based model.txt.ipynb
49. Unsupervised Lexicon-Based Models VADER Lexicon based model.txt.ipynb		49. Unsupervised Lexicon-Based Models VADER Lexicon based model.txt.ipynb
5-tfidfvectorizer.ipynb		5-tfidfvectorizer.ipynb
5. Building a Text Normalizer.ipynb		5. Building a Text Normalizer.ipynb
50. Classifying Sentiment with Supervised Learning Using SVM & Logistic Regression.ipynb		50. Classifying Sentiment with Supervised Learning Using SVM & Logistic Regression.ipynb
51. Classifying Sentiment with Supervised Deep Learning Models.ipynb		51. Classifying Sentiment with Supervised Deep Learning Models.ipynb
52. Advanced Supervised Deep Learning Models Using LSTM.ipynb		52. Advanced Supervised Deep Learning Models Using LSTM.ipynb
53. Sentiment Causal Analysis - Model Interpretation.ipynb		53. Sentiment Causal Analysis - Model Interpretation.ipynb
54. Sentiment Causal Analysis - Topic Models.ipynb		54. Sentiment Causal Analysis - Topic Models.ipynb
55. Sentiment Analysis - Text Classification with Universal Embeddings.ipynb		55. Sentiment Analysis - Text Classification with Universal Embeddings.ipynb
56. Transfer Learning with Different Universal Sentence Embeddings NNLM, USE.ipynb		56. Transfer Learning with Different Universal Sentence Embeddings NNLM, USE.ipynb
6-manual-tfidf-feature-matrix.ipynb		6-manual-tfidf-feature-matrix.ipynb
6.Pairwise document similarity matrix.ipynb		6.Pairwise document similarity matrix.ipynb
7-extracting-features-for-new-documents.ipynb		7-extracting-features-for-new-documents.ipynb
7. Clustering documents using similarity features.ipynb		7. Clustering documents using similarity features.ipynb
8-compare-pairwise-document-similarity-on-tfidf.ipynb		8-compare-pairwise-document-similarity-on-tfidf.ipynb
9-document-clustering-with-similarity-features.ipynb		9-document-clustering-with-similarity-features.ipynb
LECENSE.md		LECENSE.md
README.md		README.md
contractions.py		contractions.py
model_evaluation_utils.py		model_evaluation_utils.py
new.file		new.file
text_normalizer.py		text_normalizer.py

taimourmourad/Text-Analytics-with-Python-A-Practitioners-Guide-to-Natural-Language-Processing-by-Dipanjan-Sarkar

Folders and files

Latest commit

History

Repository files navigation

Demo Project REAME

Heading 2

About

Topics

Resources

Stars

Watchers

Forks

Releases

Packages 0

Languages

Packages