The location “hotel bar” is frequently used in various contexts as well. One of the characters appears to be a “lonely hit-man” (aren’t they always?).
With bigrams we see a full city name (likely the location of the movie), character names, actor/actress names and some of the plot exposed such as “hit man”, “straight man” and “odd couple”.
Looking at the bigram results above we get more insight into the movie review comments than unigrams alone could provide. If the underscore is omitted then the word cloud will cram words between bigrams making it less readable. Note: I added an underscore to link bigrams together to make the word cloud easier to read. Stuff a Python dictionary with the bigram and bigram measure raw frequency score.Use the NLTK Bigram Collocation finder to determine the frequency of each bigram (explained below).Use lemmatization to consolidate closely redundant words.Remove punctuation and other characters like etc.In addition to the overall steps above, the list of bigrams were further processed with the following steps: In the document(s) that you analyze you may see the same phrase appear multiple times or it may appear only once. Print the top N frequently occurring bigrams to the screenĬollocations are words the occur together at some frequency.Call the NLTK collocations function to determine the most frequently occurring bigrams.Tokenize the raw text string into a list of words where each entry is a word.Read a text file as a string of raw text.The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011).įirst, the overall steps I used to get the most frequently occurring bigrams is to: Learning Word Vectors for Sentiment Analysis. The source for The Matador movie reviews below is: Internet Movie Database (IMDb) collected by Andrew L. Below you’ll notice that word clouds with frequently occurring bigrams can provide greater insight into raw text, however salient bigrams don’t necessarily provide much insight. Py.savefig("./masked_workcloud.In the prior blog post we received mixed results trying to summarize movie review comments using frequently occurring unigrams and salient unigrams. Twitter_mask= np.array(Image.open('./sitr.jpg')) #sitr.jpg image name I will use this mask to mask my word cloud.įrom wordcloud import WordCloud,STOPWORDS
You can discover it’s properties from it’s website.
wordcloud is a word cloud generator module. Lastly, we will create word cloud and mask it with our mask.
Now we will read tweets that we saved before into “genekellyfans.txt” albeit, you can read it from API directly. We can get it from twitter developer page.
Tweepy is a library that makes easy to access Twitter API. Tweets = api.user_timeline(screen_name="genekellyfans",count=1000) As a fan of him, I will get tweets from twitter, GeneKellyFans pageĪccess_token_secret="#ACCESS_TOKEN_SECRET"Īuth = tweepy.OAuthHandler(consumer_key, consumer_secret)Īuth.set_access_token(access_token, access_token_secret) Who is Gene Kelly? He was an actor and I like his movies. Tags are usually single words, and the importance of each tag is shown with font size or color.
*Masking your word cloud with shape that you want WHAT IS WORD CLOUD?Ī tag cloud ( word cloud, or weighted list in visual design) is a novelty visual representation of text data, typically used to depict keyword metadata (tags) on websites, or to visualize free form text. Though a cloud is made of water drops or ice crystals floating in the sky, in this tutorial, we will make a cloud by words from our tweets.