Machine Learning, Deep Learning, NLP, LLM's & AI

Machine Learning, Deep Learning, NLP, LLM's & AI

Machine Learning, Deep Learning, NLP, LLM's & AI

Machine Learning & NLP SEO Services:

  1. Keyword Extraction: Extracting relevant keywords from large datasets using machine learning algorithms such as models like BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly Optimized BERT Pretraining Approach), or XLNet (Extreme Large-scale Neural network Language model)
from transformers import BertTokenizer, BertModel
import torch
 
# Load pre-trained BERT model and tokenizer
model_name = 'bert-base-uncased'
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertModel.from_pretrained(model_name)
 
# Example text data
text = "Keyword extraction is the task of identifying important words or phrases from text."
 
# Tokenize the text
inputs = tokenizer(text, return_tensors='pt')
outputs = model(**inputs)
 
# Extract the embeddings for each token
token_embeddings = outputs.last_hidden_state[0]
 
# Convert to numpy array for further processing
token_embeddings = token_embeddings.detach().numpy()
 
# Print token embeddings
for token, embedding in zip(tokenizer.tokenize(text), token_embeddings):
    print(f"Token: {token}, Embedding: {embedding[:5]}...")  # Display first 5 elements for brevity
Token: key, Embedding: [-0.3300163  -0.39773816 -0.7331577  -0.16246746 -0.35335115]...
Token: ##word, Embedding: [-0.32510367 -0.211187   -0.4033796  -0.12039164  0.29958674]...
Token: extraction, Embedding: [-0.6109904  -0.04703491 -0.05116182 -0.31660753  0.75384265]...
Token: is, Embedding: [-0.5650322   0.2509209  -0.49352887 -0.20091213  0.71768343]...
Token: the, Embedding: [-0.08126638  0.19776767 -0.41648164 -0.28400514  0.21632487]...
Token: task, Embedding: [-0.10088038 -0.1992302  -0.4438328  -0.00524061  0.06786112]...
Token: of, Embedding: [ 0.1734445  -0.08642493 -0.6627809  -0.30654463  0.11002157]...
Token: identifying, Embedding: [-0.7350841   0.19656911 -0.894021    0.00897935  0.3540546 ]...
Token: important, Embedding: [-0.48176876  0.2694786   0.21553569 -0.4727178   0.5036165 ]...
Token: words, Embedding: [-0.5259429   0.272185   -0.3521507  -0.5490284   0.14939153]...
Token: or, Embedding: [ 0.1445177   0.9307165   0.03141395 -0.43853498  0.2388922 ]...
Token: phrases, Embedding: [ 0.9836977   0.48321965  0.6874629  -0.0049537   0.6204151 ]...
Token: from, Embedding: [ 0.8563778   0.14313772 -0.32211912 -0.11594377 -0.15347044]...
Token: text, Embedding: [ 0.40585446  0.30699936  0.02127423 -0.00826865  0.63988954]...
Token: ., Embedding: [ 0.408903    0.6881197  -0.11956017 -0.43798736  0.2487345 ]...
  1. Topic Modeling: Identifying underlying topics or themes in a dataset using topic modeling techniques like Probabilistic Latent Semantic Analysis (pLSA): An extension of LSA that incorporates probabilistic reasoning to improve topic coherence.
  2. Topic Modeling with Deep Learning: Using deep learning architectures to model the underlying topics in text data such as Deep Topic Model, Variational Autoencoder, or Neural Topic Model.
  3. Sentiment Analysis: Analyzing text data to determine the sentiment or emotional tone of the content using machine learning models such as Naive Bayes classifier.
  4. Entity Extraction: Extracting entities such as names, locations, and organizations from unstructured text data using NLP and machine learning techniques such as Stanford CoreNLP’s Named Entity Recognition (NER) module.
  5. Classification Modeling: Building classification models to categorize text data into predefined categories using machine learning algorithms such as Support Vector Machines (SVMs).
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, classification_report
 
# Example dataset
data = {
    'text': ["This is a positive comment", "This is a negative comment", "This is another positive comment", "This is another negative comment"],
    'label': [1, 0, 1, 0]
}
 
df = pd.DataFrame(data)
X = df['text']
y = df['label']
 
# Convert text data to TF-IDF features
vectorizer = TfidfVectorizer()
X_tfidf = vectorizer.fit_transform(X)
 
# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(X_tfidf, y, test_size=0.2, random_state=42)
 
# Create and train the SVM model
model = SVC()
model.fit(X_train, y_train)
 
# Predict and evaluate
y_pred = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred)}")
print(f"Classification Report:\n{classification_report(y_test, y_pred)}")
 
# Predict category for a new text
new_text = ["This is a new positive comment"]
new_text_tfidf = vectorizer.transform(new_text)
predicted_label = model.predict(new_text_tfidf)
print(f"Predicted Label: {predicted_label[0]}")
Accuracy: 0.0
Classification Report:
              precision    recall  f1-score   support
 
           0       0.00      0.00      0.00       1.0
           1       0.00      0.00      0.00       0.0
 
    accuracy                           0.00       1.0
   macro avg       0.00      0.00      0.00       1.0
weighted avg       0.00      0.00      0.00       1.0
 
Predicted Label: 1
  1. Text Classification: Classifying text into predefined keywords/topics/sentiments/segments/etc.
    • Latent Semantic Analysis (LSA): Latent Semantic Analysis (LSA) breaks down complex text documents into smaller parts, each highlighting a unique perspective on the original meaning. By doing so, LSA distills the data into a concise representation of underlying concepts or themes, making it easier to understand and analyze.
    • Latent Dirichlet Allocation (LDA): Latent Dirichlet Allocation (LDA) is a mathematical model that represents complex texts as combinations of underlying themes or topics. Each topic is defined by its unique set of words and their frequencies, allowing for a deeper understanding of the text’s meaning and structure.
    • Non-negative Matrix Factorization (NMF): Non-negative Matrix Factorization (NMF) takes a matrix of word frequencies in a corpus and breaks it down into two smaller matrices that represent different topics. These topics can then be combined to generate new and more informative representations of the text.
  2. Named Entity Recognition (NER): Identifying named entities such as people, places, and organizations in unstructured text data.
  3. Tokenization: Tokenization helps identify the most common words and phrases in your content. By analyzing these frequencies, you can refine your content to incorporate relevant keywords that match your audience’s search habits.
  4. Search Query Prediction: Use machine learning models like recurrent neural networks (RNNs) or long short-term memory (LSTM) networks to predict future search queries based on historical data, trends, and patterns.
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Embedding
 
# Example historical data (extended list of queries)
queries = [
    "how to cook pasta",
    "best pasta recipes",
    "pasta cooking tips",
    "easy pasta dishes",
    "how to bake bread",
    "best bread recipes",
    "bread baking tips",
    "easy bread recipes",
    "how to make soup",
    "best soup recipes",
    "soup cooking tips",
    "easy soup dishes"
]
 
# Preprocess data
tokenizer = tf.keras.preprocessing.text.Tokenizer()
tokenizer.fit_on_texts(queries)
sequences = tokenizer.texts_to_sequences(queries)
data = tf.keras.preprocessing.sequence.pad_sequences(sequences, padding='post')
 
# Define model
vocab_size = len(tokenizer.word_index) + 1
model = Sequential([
    Embedding(input_dim=vocab_size, output_dim=64),
    LSTM(128, return_sequences=True),
    LSTM(128),
    Dense(vocab_size, activation='softmax')
])
 
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
 
# Training data (X: all but last word, y: last word)
X, y = data[:, :-1], data[:, -1]
 
# Train model (increase epochs for better learning)
model.fit(X, y, epochs=50, batch_size=4)
 
# Predict next query
new_query = "how to cook"
new_seq = tokenizer.texts_to_sequences([new_query])
new_seq_padded = tf.keras.preprocessing.sequence.pad_sequences(new_seq, maxlen=X.shape[1], padding='post')
predicted = model.predict(new_seq_padded)
 
# Get the predicted word
predicted_index = np.argmax(predicted)
if predicted_index in tokenizer.index_word:
    predicted_word = tokenizer.index_word[predicted_index]
    print(f"Predicted next word: {predicted_word}")
else:
    print("Predicted index not found in tokenizer's word index.")
...
...
Epoch 45/50
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1719
Epoch 46/50
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1675
Epoch 47/50
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.3407
Epoch 48/50
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2629
Epoch 49/50
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.2647 
Epoch 50/50
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.2208 
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 91ms/step
Predicted next word: pasta
  1. Keyword Ranking Prediction: Develop a keyword ranking predictor using machine learning models like gradient boosting machines (GBMs) or xGaussian processes to predict the likelihood of a given keyword appearing at the top of search engine results pages.

AI SEO Services:

  1. Knowledge Graph Construction: Building knowledge graphs that represent complex relationships between entities, concepts, and attributes.
import gensim
from gensim import corpora
from gensim.models import LdaModel
from gensim.parsing.preprocessing import preprocess_string
 
# Example text data
documents = [
    "How to cook pasta and best pasta recipes",
    "Bread baking tips and easy bread recipes",
    "Soup cooking tips and best soup recipes"
]
 
# Preprocess the text data
texts = [preprocess_string(doc) for doc in documents]
 
# Create a dictionary representation of the documents
id2word = corpora.Dictionary(texts)
 
# Create a corpus (Bag of Words representation)
corpus = [id2word.doc2bow(text) for text in texts]
 
# Build the LDA model
lda_model = LdaModel(corpus=corpus, id2word=id2word, num_topics=2, random_state=42)
 
# Print the topics
for topic_idx, topic in enumerate(lda_model.print_topics(num_topics=2)):
    print(f"Topic {topic_idx}: {topic}")
 
# Extract keywords
for idx, topic in lda_model.show_topics(formatted=False, num_topics=2):
    print(f"Topic {idx}:")
    for word, weight in topic:
        print(f" {word}: {weight}")
 
Topic 0: (0, '0.140*"recip" + 0.140*"soup" + 0.137*"tip" + 0.132*"bread" + 0.114*"best" + 0.108*"cook" + 0.083*"bake" + 0.078*"easi" + 0.068*"pasta"')
Topic 1: (1, '0.182*"pasta" + 0.173*"recip" + 0.126*"cook" + 0.117*"best" + 0.093*"bread" + 0.086*"tip" + 0.081*"soup" + 0.075*"easi" + 0.068*"bake"')
Topic 0:
 recip: 0.14024780690670013
 soup: 0.14009582996368408
 tip: 0.13667701184749603
 bread: 0.13188967108726501
 best: 0.11419231444597244
 cook: 0.10770779103040695
 bake: 0.08334696292877197
 easi: 0.07829634845256805
 pasta: 0.06754627078771591
Topic 1:
 pasta: 0.18163330852985382
 recip: 0.17267774045467377
 cook: 0.1260157823562622
 best: 0.11703573912382126
 bread: 0.09252769500017166
 tip: 0.08589794486761093
 soup: 0.08116340637207031
 easi: 0.07502134889364243
 bake: 0.0680270791053772
  1. AI and Machine Learning Integration AI Content Generation:: Utilizing AI tools for content creation and optimization.
  • e.g. NMF module from scikit-learn:
data = pd.read_csv('your_data.csv')
 
# Split data into training and testing sets
X_train, X_test = train_test_split(data, test_size=0.2)
 
# Create an instance of the NMF class
nmf = NMF(n_components=3)
 
# Fit the model to the training data
nmf.fit(X_train)
 
# Predict topics for the test data
topics = nmf.transform(X_test)
  1. Conversational AI: Developing conversational AI models that can understand natural language inputs and generate responses.
  2. Intent Detection: Detecting user intent behind search queries or voice commands using machine learning and NLP techniques.
  3. Predictive SEO: Using machine learning to predict and adapt to search trends.
  • e.g. pre-trained BERT model:
import pandas as pd
from transformers import BertTokenizer, BertModel
 
# Load your dataset
data = pd.read_csv('your_data.csv')
 
# Create a Bert tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
 
# Create a Bert model
model = BertModel.from_pretrained('bert-base-uncased')
 
# Fine-tune the model on your specific task
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model.to(device)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
 
for epoch in range(5):
    for batch in data:
        input_ids, attention_mask = batch['input_ids'], batch['attention_mask']
        labels = batch['labels']
 
        optimizer.zero_grad()
 
        outputs = model(input_ids, attention_mask)
        loss = loss_fn(outputs, labels)
        loss.backward()
        optimizer.step()
 
    print(f"Epoch {epoch}: Loss={loss.item():.4f}")
  1. Conversational Search Optimization: Structuring content to align with conversational search queries, which are becoming more common with voice search and AI chatbots.
  2. Voice Search Optimization Natural Language Processing: Creating content that aligns with natural language queries used in voice search.
  3. Long-Tail Keyword Integration: Incorporating long-tail keywords that match conversational search patterns.