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We'll use it to select our vocabulary and build the word vectors. When you break up the reviews into words, use.
Exercise: Create a dictionary called word2idx that maps each word in the vocabulary to an index. The data we're using has already been preprocessed a bit and we know it uses only lower case characters. As Andrew noted, most of the words in the vocabulary are rarely used so they will have little effect on our predictions.
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This resulting count is known as a bag of words. I don't really want to meet men for just sex and and then have them go "surprise, I want more!
It's happened. Sex only. You should have seen how to do this in Andrew's lesson. Try to implement it here using the Counter class.
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That's because there are many words tied for that of counts, and the Counter class does not guarantee which one will be returned in the case of a tie. Xxx women wanting women loking for sex Adult looking real sex SC Darlington Horny bbw ready local horney sdx Women seeking men for sex in Klemme city Amateurs swingers in Hobart Naughty woman want nsa Sandy Swingers partner Escondido Beautiful older ladies wants nsa Texas Yoni massage Tempe Ladies seeking hot sex PA Carlisle barrack Embree, Newfoundland moms looking sex Columbus Georgia adult chats Cheap sluts in Mitocu Balan Adults friends want bbw looking for sex Mature lonely wanting phone sex chat Columbus girls fucking Great sex will lick long if you suck long Housrwives for a lation, or asian women for sex wex friendship To e fat adult hookupss from your Anchorage Mature sex La Porte contact West Jordan Utah sex massage Lesington lady wanting woman funck Send A Message Ladies looking sex tonight Pleasant Arkansas Lets experiment w4m Heyto the rest of the people here looking for some wilddd sex.
If you want the reviews as a Numpy array, use reviews.
The word vectors will have elements representing words in the total vocabulary. The first word in vocab has index 0, the second word has index 1, and so on.
The reviews are stores in the reviews Lexnigton DataFrame. Check out Andrew's notebook and video for more about this. That's so we treat different variations of the same word, like The, the, and THE, all the same way.
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We can use this to judge if is too few. First we need to make a mapping of word to index, pretty easy to do with a dictionary comprehension.
We are probably fine with this of words. We'll use this count to create a vocabulary we'll use to encode the review data.
If the second position represents the word 'the', for each review we'll count up the of tonihht 'the' appears in the text and set the second position to that count. Below, we'll sort vocab by the count value and keep the most frequent words.
I am honest to god looking for real, true no strings sex. If we were working from raw data, where we didn't know it was all lower case, we would want to add a step here to convert it.
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I think it's fair to say this is a tiny proportion. It takes care of most of the details for you.
TFLearn makes it simpler to build networks just by defining the layers. I'll show you examples as we build the input data from the reviews data. Now for each review in the data, we'll make a word vector.
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If the last word is pretty common, we probably need to keep more words. We'll start off by importing all the modules we'll need, then load Lexungton prepare the data. You can iterate through the rows in the DataFrame with for idx, row in reviews. Note: When you run, you may see a different word from the one shown above, but it will also have the value