Kick off with 1970s jazz, coffee, and sentiment analysis! Join Anastasia, Sophia, Irene, Ethan, and Olivia as we build an LSTM that feels IMDB reviews. Anastasia leads two demos, Ethan drops flirty code. Support at PayPal.me/DailyAIWizard! Get ready for Day 83: Model Saving! Subscribe, like, share your ai_sentiment_analysis.py!
pay a coffee: https://buymeacoffee.com/dailyaiwizard
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.
05:58Wizards, 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.
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