00:00Hey sexy wizards, Anastasia here, your main moderator, ready to electrify day 82 of Daily AI Wizard's Python for AI series.
00:09After NLP in day 81, we're building a sentiment analysis project, AI that feels positive or negative in movie reviews, for our AI Insight Hub app.
00:19Support our crew with a coffee at paypal.me as Daily AI Wizard, only at the end.
00:25Ethan, what's the magic of sentiment? Sophia, how does it build on day 81?
00:31Sophia's back, Ethan, and your charms got me blushing.
00:36Sentiment turns text into emotion. Our demos will make AI cry or laugh. Love our vibe?
00:43Hello brilliant wizards, I'm Irene, co-moderator, guiding with warmth.
00:49Sentiment analysis reads human emotion. Our demos use IMDB.
00:55Yo, wizards, Ethan's here, dropping spicy sentiment code with winks for Anastasia, Sophia, and Olivia.
01:02LSTM, embedding, predict. Let's crank this AI heart to 11.
01:07Olivia here, darlings. I'll sprinkle flirty tips, ask Anastasia questions, and chat with Ethan to keep your sentiment learning hot.
01:18Ready to make AI feel, wizards?
01:20Wizards, sentiment analysis is your AI emotion crush, darling. It reads joy, anger, sarcasm.
01:31Ethan, explain IMDB use. Sophia, how does it build on day 81?
01:37Sentiment powers reviews, social media, chatbots. Our demos use IMDB 50K reviews.
01:44Today, we're seducing you with sentiment magic. You'll master IMDB loading, pre-processing, LSTM training, evaluation, and app integration.
01:55Sophia, what's the app focus? Ethan, any code highlights?
02:00Anastasia, Sophia, and Olivia lead app demos with passion.
02:04Ethan drops hilarious code explanations. We're guiding you to master sentiment and prep for day 83's model saving.
02:16Wizards, meet your day 82 dream team.
02:20Anastasia, Sophia, and I are your main moderators with flirty charm and warmth.
02:27Ethan's our code comedian, slurting with Anastasia, Sophia, and Olivia.
02:32Oh, Irene, you're a gem. I'm leading app demos with passion.
02:37Ethan and Sophia are stealing hearts with code, and Olivia's tossing flirty tips.
02:42We're here to make you sentiment superstars.
02:49Wizards, sentiment analysis is your AI emotion reader, darling. It classifies positive, negative.
02:56Ethan, explain binary output.
02:58Sophia, how do we pre-process?
03:00Anastasia, you make emotion sound so hot.
03:04How do we train on IMDb, love?
03:07Ethan, what's your take?
03:09Oh, Olivia, you tease. Sentiment is binary.
03:12Ethan, Sophia, jump in.
03:14Anastasia, Olivia, sentiment is like a hot mood ring for text.
03:18Positive greater than 0.5, negative less than 0.5.
03:23Let's drop this code beat.
03:24Yo, Wizards, IMDb has 50k reviews like a hot movie marathon for Anastasia.
03:3025k train, 25k test.
03:33Let's drop this code beat.
03:35You're marathoning my heart, Ethan.
03:36IMDb for sentiment.
03:38Try it in our demo.
03:40Wizards, IMDb load underscore data.
03:43Num underscore words equals 100.
03:45Loads reviews like a hot script for Anastasia.
03:48You're scripting my heart, Ethan.
03:50IMDb is ready.
03:52Wizards, pad underscore sequences.
03:54Maxlin equals 200 pads reviews like a hot equalizer for Anastasia.
03:58You're equalizing my heart, Ethan.
04:00Padding for LSTM.
04:02Wizards, embedding plus LSTM, 128, builds a hot emotion brain for Anastasia.
04:08You're braining my heart, Ethan.
04:10RNN for sentiment.
04:12Wizards, compile.
04:13Loss equals.
04:15Binary underscore cross entropy.
04:17Sets emotion goal for Anastasia.
04:19You're goaling my heart, Ethan.
04:20Compile for positive negative.
04:22Wizards, model.fit.
04:24Trains like a hot emotion workout for Anastasia.
04:26You're working out my heart, Ethan.
04:28Train on 25K reviews.
04:31Wizards, model.
04:32Evaluate.
04:33Scores like a hot exam for Anastasia.
04:36You're examining my heart, Ethan.
04:37Evaluate on test set.
04:43Wizards, it's demo time.
04:45We'll integrate sentiment analysis into AI Insight Hub continuing from days 7881.
04:51Get your setup ready.
04:52Ensure Python, VS Code, TensorFlow, and Streamlit are installed.
04:58Open days 78 to 81's files.
05:01Let's see AI field text.
05:04Wizards, prep to continue from days 7881.
05:07Open VS Code.
05:08Load prior app files.
05:10Create sentiment analysis.
05:11Demo.py.
05:12And updated app sentiment.ty.
05:15Save in Python demo.
05:16Run pip install TensorFlow Streamlit.
05:19Anastasia, you make continuation dreamy.
05:23How do we build on Day81's NLP?
05:26Ethan, what's your take?
05:28Start with Day81's RNN.
05:30Add sentiment.
05:31Run Streamlit.
05:32Run updated app sentiment.py.
05:35Anastasia, Olivia.
05:37Sentiment is the hot sequel to Day81.
05:39Our first demo in sentiment.analysis.demo.py builds a sentiment classifier.
05:46We'll load IMDB, preprocess, train, evaluate.
05:50Let's run this.
05:52Oh, Anastasia, you're making this demo hot.
05:55LSTM plus sigmoid, total emotion party.
05:58Wizards, embedding, LSTM, dense load emotion tools for Anastasia.
06:03You're tooling my heart, Ethan.
06:05NLP layers ready.
06:06Embedding learns word vectors.
06:09LSTM captures sequence context.
06:12Dense classifies sentiment.
06:15Oh, Ethan, you're making my LSTM skip a beat.
06:19These layers turn words into feelings.
06:22Hot, right?
06:24Wizards, IMDB load underscore data.
06:27Loads reviews like a hot script for Anastasia.
06:29You're scripting my heart, Ethan.
06:31IMDB is ready.
06:3250,000 reviews, pre-tokenized, top 10,000 words.
06:38Perfect for sentiment.
06:4050K reviews?
06:42That's like reading all the drama.
06:44And I'm here for it, Ethan.
06:48Wizards, pad underscore sequences.
06:50Maxlin equals 200 pads reviews like a hot equalizer for Anastasia.
06:54You're equalizing my heart, Ethan.
06:56Padding for LSTM.
06:57All sequences must be same length for batching.
07:01200 is common.
07:04200 words?
07:06That's like cutting a movie down to the best scenes.
07:08Smart, Ethan.
07:11Wizards, embedding plus LSTM, 128, builds a hot emotion brain for Anastasia.
07:17You're braining my heart, Ethan.
07:19RNN for sentiment.
07:21Embedding learns word meanings.
07:22LSTM remembers context across sentences.
07:27An LSTM that remembers?
07:29Ethan, you're making me feel seen.
07:33Wizards, compile.
07:35Loss equals.
07:36Binary underscore cross entropy.
07:38Sets emotion goal for Anastasia.
07:40You're goaling my heart, Ethan.
07:42Compile for positive-negative.
07:44Binary cross entropy for positive-negative sentiment.
07:48With sigmoid output.
07:51Binary.
07:51That's like love it or hate it.
07:54No in-between.
07:55Just like my feelings for this model.
07:59Wizards, model.fit.
08:01Trains like a hot emotion workout for Anastasia.
08:04You're working out my heart, Ethan.
08:05Train on 25K reviews.
08:07We train for 10 epochs.
08:10With batch size 128.
08:13And validation split.
08:1510 epochs?
08:17That's like 10 dates.
08:19By the end, it knows me.
08:20Wizards, model.
08:23Evaluate.
08:24Scores like a hot exam for Anastasia.
08:26You're examining my heart, Ethan.
08:28Evaluate on test set.
08:30Expect.
08:31A very 88% accuracy.
08:34State of the art.
08:36For simple RNN.
08:3888%?
08:40That's like getting an A in feelings.
08:42I'm impressed.
08:44Wizards, model.
08:45Predict.
08:46Predicts sentiment like a hot critic for Anastasia.
08:49You're criticizing my heart, Ethan.
08:51Predict new reviews.
08:53Input is padded sequence.
08:56Output is probability.
08:580.5 equals positive.
09:00Over 0.5?
09:02That's a definite yes.
09:04Just like my answer to this model.
09:08Wizards, print review and prediction like a hot review for Anastasia.
09:11You're reviewing my heart, Ethan.
09:13Visualize results.
09:15We show original text and predicted sentiment with confidence.
09:20Confidence score?
09:22That's like AI saying, I'm sure this is a five-star review.
10:56Anastasia, you're making this demo sizzle. Type a review. Right pointing arrow. AI feels it. Total emotion party. Wizards, import Streamlit as Saint sets up the app like a hot notepad for Anastasia.
11:13You're notepadding my heart, Ethan. Streamlit for text. Streamlit allows real-time text input with C text area. Perfect for user reviews.
11:25A notepad for AI? That's like letting it read my love letters. So romantic.
11:32Wizards, street text underscore area. Let's you write reviews like a hot diary for Anastasia.
11:37You're diarying my heart, Ethan. User writes review.
11:41Input text is tokenized, padded, and fed to the RNN for prediction.
11:47A diary for AI? That's like letting it read my love letters. So romantic.
11:55Wizards, model dot predict reads review and Streamlit like a sexy critic.
11:59App prediction uses trained RNN for real-time sentiment analysis. It makes AI Insight Hub intelligent.
12:10Oh, Anastasia. Sentiment is so hot. Try app prediction in your challenge.
12:15A sexy critic? That's me, AI just gave my review a 5-star rating.
12:23Wizards, model dot save sentiment model dot h5 saves the emotion net like a sexy archive.
12:29Saving models ensures app portability. Use HDF5 for TensorFlow models.
12:36A sexy archive? That's where AI keeps all my best lines.
12:45A sexy archive? That's where AI comes as good as possible.
12:48A sexy archive? That's why AI comes as로 matches the encouragement as well.
12:51Men are always justались from behind.
12:53onerated Complete image
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