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The BFI did a re-release of it recently on Blu-ray which is just superb, and I think people who haven't seen it will be surprised at just how forward-looking it is. It's monochrome but it still manages to be genuinely alarming, the surgery sequences in it are really extraordinary. Audition Trevor was learning Japanese at the time and we just thought it was a Japanese movie.
For the first 20 minutes it's like this bizarre rom-com in which there's a guy who can't get a girlfriend so he starts these theatrical auditions. The audience had no idea where it was going.
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It scared the life out of me, but I loved it. The Evil Dead It had the whole 'video nasty' reputation but I didn't have a VHS player, so my mate Phil and I went to see it and it was just brilliant. The genius of it is that it's both funny and scary at the same time. That's really something.
Review: Philip French on We Own the Night | Film | The Guardian
Don't Look Now It's brilliantly adapted from a short story by Daphne Du Maurier, which the screenwriters managed to find so much in. So much of Don't Look Now is to do with the screenplay - stuff that isn't in the original story. I just think director Nicolas Roeg is a genius - that film is heartbreaking, really moving, and it proves that things become scarier when you're involved with the characters instead of someone leaping out of a cupboard and shouting 'boo'. Type keyword s to search. Now that the LDA model is built, the next step is to examine the produced topics and the associated keywords.
Each bubble on the left-hand side plot represents a topic. The larger the bubble, the more prevalent is that topic. A good topic model will have fairly big, non-overlapping bubbles scattered throughout the chart instead of being clustered in one quadrant.
A model with too many topics, will typically have many overlaps, small sized bubbles clustered in one region of the chart. Alright, if you move the cursor over one of the bubbles, the words and bars on the right-hand side will update. These words are the salient keywords that form the selected topic. Given our prior knowledge of the number of natural topics in the document, finding the best model was fairly straightforward.
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You only need to download the zipfile, unzip it and provide the path to mallet in the unzipped directory to gensim. See how I have done this below. My approach to finding the optimal number of topics is to build many LDA models with different values of number of topics k and pick the one that gives the highest coherence value. Picking an even higher value can sometimes provide more granular sub-topics.
If the coherence score seems to keep increasing, it may make better sense to pick the model that gave the highest CV before flattening out. This is exactly the case here. One of the practical application of topic modeling is to determine what topic a given document is about.
To find that, we find the topic number that has the highest percentage contribution in that document. Sometimes just the topic keywords may not be enough to make sense of what a topic is about. So, to help with understanding the topic, you can find the documents a given topic has contributed to the most and infer the topic by reading that document. The tabular output above actually has 20 rows, one each for a topic.
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It has the topic number, the keywords, and the most representative document. Finally, we want to understand the volume and distribution of topics in order to judge how widely it was discussed. The below table exposes that information.
We started with understanding what topic modeling can do. You saw how to find the optimal number of topics using coherence scores and how you can come to a logical understanding of how to choose the optimal model. Finally we saw how to aggregate and present the results to generate insights that may be in a more actionable.
Hope you enjoyed reading this. I would appreciate if you leave your thoughts in the comments section below. Hope you will find it helpful. Contents 1. Introduction 2. Prerequisites — Download nltk stopwords and spacy model 3. Import Packages 4. What does LDA do? Prepare Stopwords 6. Import Newsgroups Data 7. Remove emails and newline characters 8. Tokenize words and Clean-up text 9. Creating Bigram and Trigram Models Remove Stopwords, Make Bigrams and Lemmatize Create the Dictionary and Corpus needed for Topic Modeling Building the Topic Model View the topics in LDA model Compute Model Perplexity and Coherence Score Visualize the topics-keywords How to find the optimal number of topics for LDA?
Finding the dominant topic in each sentence Find the most representative document for each topic Topic distribution across documents.