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Ranking state is definitely an under appreciated factor. And yes, depending on state and size of size, hitting the site with tons of content can be a great shock to Google, and indeed if you see a positive change in ranking state, hammer that site with content for as long as possible. Appreciate the concrete examples, Koray!
Thank you @Topicalauthority for sharing this lecture and also placing additional resources in the desdciption for our research. smh always such practical information that it is so appreciated!
Is there a way to tell the difference between a neutral ranking state and a state where you hit a ceiling in your ranking scale following a positive ranking state? thanks
Hi Korey, thank you so much for such great insights, I was wondering if someone is looking for SEO services from your team, where should he go to see the pricing plan of how much you guys charge monthy - Hoping to get a reply from you! Thanks.
Could you please talk about the broad algorithmic changes that happened to combat Ai spam. What do you think they are targeting specifically. Is it the absence of the relationship between the source context and the author section.
I've got full AI sites just doing fine and not dropping. Higher CTR and ranking and lower impressions. AI or not doesn't matter. It has to be useful though. It can't be Text based AI spam.
Koray brother please make a content on the topic what google want like which type of content google want and how i can write more efficiently ,how i can add more quality in the content
@topicalauthority did a YT video in the last month that was very helpful and believe/hope will be helpful for your question. about writing...in the video, Koray provided the below list and shared to start with the following three concepts: Named Entity Recognition, Part of Speech Tag, Sentiment Analysis 40 NLP Concepts to know and use in SEO Efforts while configuring customized GPTs via #openai 1. Probabilistic Language Modeling: Estimating probabilities of word sequences. 2. Corpus-Based Linguistic Analysis: Analyzing language through extensive text collections. 3. End-to-End Neural Machine Translation: Direct translation using deep neural models. 4. Syntactic Part-of-Speech Tagging: Assigning grammatical categories to tokens in sentences. 5. Morphological Lemmatization: Converting words to their base or dictionary forms. 6. AI-based Question Answering Systems: Generating automated responses to queries. 7. Natural Language Dialogue Systems: Developing AI-driven conversational interfaces. 8. Automated Text Categorization: Classifying text into predefined groups algorithmically. 9. Automated Speech Recognition: Transcribing spoken language into text. 10. Computational Natural Language Understanding: Interpreting human language using machines. 11. Affective Computing in Sentiment Analysis: Analyzing subjective information in texts. 12. Anaphora Resolution in Co-reference Analysis: Identifying references to the same entity. 13. Deep Neural Networks in NLP: Applying layered neural architectures for language processing. 14. Self-Attention in Transformer Models: Focusing on different parts of sequences in models. 15. Algorithmic Natural Language Generation: Producing text from structured data. 16. Cross-Lingual Machine Translation: Translating between different linguistic systems. 17. Automated Textual Summarization: Condensing long text into shorter versions. 18. Morpheme-based Stemming: Truncating words to their root forms. 19. Focused Attention Mechanisms in NLP: Enhancing focus on relevant input parts. 20. Computational Syntax Tree Parsing: Analyzing linguistic syntax structures algorithmically. 21. Translingual Model Transfer Learning: Applying language learnings across languages. 22. Pattern Matching with Regular Expressions: Using text patterns for linguistic tasks. 23. Seq2Seq Architectures: Converting input sequences to output sequences. 24. Entity Resolution in Named Entity Recognition: Classifying and identifying entities. 25. Structured Information Retrieval in NLP: Extracting specific data from texts. 26. Lexical Tokenization: Segmenting texts into individual words or phrases. 27. Synthetic Speech Generation: Converting text to speech. 28. Contextual Word Sense Disambiguation: Interpreting words based on context. 29. Distributed Semantic Representation with Word Embeddings: Contextual word vector representations. 30. Latent Dirichlet Allocation in Topic Analysis: Discovering abstract topics in text. 31. Bidirectional Recurrent Neural Networks in NLP: Processing sequences in both directions. 32. Ethical Considerations in NLP: Addressing moral implications in language technology. 33. Data Normalization in NLP: Standardizing text data for processing. 34. Generative Pretrained Transformer for Text Generation: Generating coherent text using an autoregressive model. 35. NLP in Computational Sociolinguistics: Analyzing social media language use. 36. Linguistic Feature Extraction: Identifying and using language attributes for machine learning. 37. Syntactic Dependency Analysis: Parsing grammatical structures in sentences. 38. Sentiment Lexicon Creation: Building databases for sentiment-related language analysis. 39. Bidirectional Encoder Representations from Transformers: Context-understanding deep learning model. 40. Conversational Agents and Systems: Systems that interact using natural language.
