Invented by Richard Israel Mallah, Akos Lajos Balogh, Informite Inc, Marketmuse Inc

The market for systems and methods for semantic keyword analysis for paid search has been experiencing significant growth in recent years. As the digital advertising landscape becomes increasingly competitive, businesses are recognizing the importance of optimizing their paid search campaigns to reach their target audience effectively. Semantic keyword analysis plays a crucial role in this process by helping advertisers identify and target relevant keywords that align with their campaign goals. Semantic keyword analysis involves analyzing the meaning and context of keywords to understand user intent better. Traditional keyword analysis primarily focuses on identifying specific keywords that users might search for. However, semantic keyword analysis goes beyond this approach by considering the broader context and understanding the user’s underlying intent. This allows advertisers to create more relevant and targeted campaigns, leading to higher click-through rates, conversions, and ultimately, a better return on investment. The market for systems and methods for semantic keyword analysis has seen significant growth due to several factors. Firstly, the increasing complexity of search engine algorithms has made it more challenging for advertisers to achieve high rankings and visibility in search results. By utilizing semantic keyword analysis tools, advertisers can gain insights into the search behavior of their target audience, helping them optimize their campaigns to match user intent more effectively. Secondly, the rise of voice search has further emphasized the importance of semantic keyword analysis. Voice search queries tend to be longer and more conversational, reflecting the way people naturally speak. Advertisers need to adapt their keyword strategies to capture these voice searches accurately. Semantic keyword analysis tools can help identify conversational keywords and long-tail phrases that are more likely to be used in voice searches, allowing advertisers to tailor their campaigns accordingly. Furthermore, the increasing availability of big data and advanced analytics tools has fueled the growth of semantic keyword analysis. Advertisers now have access to vast amounts of data, including search queries, user behavior, and demographic information. By leveraging these data sources and using sophisticated analytics tools, advertisers can gain valuable insights into the semantic relationships between keywords and user intent. This enables them to refine their keyword strategies and create more targeted campaigns. The market for systems and methods for semantic keyword analysis is highly competitive, with several companies offering innovative solutions in this space. These tools typically utilize natural language processing (NLP) algorithms and machine learning techniques to analyze and understand the meaning of keywords. They can provide valuable insights such as keyword relevance, search volume, competition level, and user intent. Some tools even offer real-time data and predictive analytics capabilities, allowing advertisers to make data-driven decisions and optimize their campaigns on the fly. In conclusion, the market for systems and methods for semantic keyword analysis for paid search is experiencing rapid growth as advertisers recognize the importance of targeting the right keywords to reach their audience effectively. Semantic keyword analysis goes beyond traditional keyword analysis by considering the broader context and understanding user intent. With the increasing complexity of search engine algorithms, the rise of voice search, and the availability of big data and advanced analytics tools, advertisers are turning to semantic keyword analysis to gain a competitive edge in the digital advertising landscape.

The Informite Inc, Marketmuse Inc invention works as follows

According to different embodiments, “a method of generating a list from one or several keywords a recommended keyword for use in paid search advertisement includes identifying via a tool one or multiple keywords to be utilized in a campaign for paid search advertising at an identified site.” A crawler may be used to collect content from multiple web content sources using one or more networks. The method can also include the tool applying an ensemble of key phrase extraction algorithms (or graph analysis algorithms) and natural language processing algorithms on the acquired content to identify a list of semantically relevant keyword ranked according to a relevance score. The tool may also generate a knowledge-graph of suggested keywords from the set. The tool may also output, based at least in part on the knowledge graph a list of recommended words to replace or complement the one or more keywords that will be used for a paid search advertising campaign.

Background for Systems and Methods for Semantic Keyword Analysis for Paid Search

In order to increase the visibility of advertising and traffic on web pages (e.g. blogs, news websites, shopping sites, etc. ), owners of web pages may engage in search engine optimization (SEO) corresponding to paid searches. Owners of web pages can engage in SEO (search engine optimization) to correspond with paid searches. Search engine optimization involves a consideration of how search engines operate, what people are searching for, the way people search (e.g. what terms people use when they search for different topics), and other factors. Owners of web pages can also optimize their targeting of advertisements to users of social networks (e.g. optimizing targeting using keywords used by social media users). Owners of websites can optimize paid search by manually researching keywords that are typically associated with the topics of their website and then using these keywords to target their internet advertising. These methods are time-consuming and cumbersome. They also have a minimal effect on web visibility.

The present solution is a new tool that allows for the analysis and research of keywords for paid search optimization. The tool provides a simple and efficient way to identify keywords that can be used in place of or as a supplement to existing keywords for paid searches.

According to different embodiments, “a method of generating a list from one or several keywords a recommended keyword for use in paid search advertisement includes identifying via a tool one or multiple keywords to be utilized in a campaign for paid search advertising at an identified site.” A crawler may be used to collect content from multiple web content sources using one or more networks. The method can also include the tool applying an ensemble of key phrase extraction algorithms (or graph analysis algorithms) and natural language processing algorithms on the acquired content to identify a list of semantically relevant keyword ranked according to a relevance score. The tool may also generate a knowledge-graph of suggested keywords from the set. The tool may also output, based at least in part on the knowledge graph a list of recommended words to replace or complement the one or more keywords that will be used for a paid search advertising campaign.

In some embodiments, receiving one or more keywords by the tool from the identified site is also included in the method.

In some embodiments, the crawler may also be used to acquire content from the website identified.

In some embodiments, applying the ensemble by the tool to the content of the identified website is also included in the method.

In some embodiments, a crawler is used to acquire content from a variety of web content sources, including websites, news articles and blog posts, as well as keyword data.

In some embodiments, one or more keyphrase extraction algorithms include a Bayesian ensemble.

In some embodiments, this method also includes the tool displaying the recommended keywords in a ranked list based on at least one of the following: an attractiveness, volume, and competition score.

In some embodiments, method outputs the enumerated keyword list ranked according to at least one relevance score.

In some embodiments, a method may also include the tool displaying a list of recommended keywords ranked according to a cost per click value.

In some embodiments, one or more keyphrase extraction algorithms comprise a Bayesian ensemble, and the ensemble performs multiple term ranking functions, including a core phrase ranking function, tail phrase ranking function and/or a hyperdictionary graph navigation algorithm.

Accordingly to various embodiments a system is provided for generating a list from one or several keywords of recommended keywords that can be used in paid search advertisements at a website identified. The system includes a tool configured on a processor for receiving an input of the one or two keywords and a list to be generated of recommended keyword to use for paid search ads at the website identified, as well as one or multiple targeting attributes to target the paid search advertisement at the website identified. The system can also include a crawler that is configured to collect content from the website identified based, at least in part, on the target attributes. The tool can be configured to apply an ensemble of key phrase extraction algorithms (or graph analysis algorithms), natural language processing algorithms (or both) to the acquired content to identify a list of semantically relevant keyword ranked according to a relevance score. The tool can be configured to create a knowledge graph from a set of semantically-relevant keywords that recommends keywords to replace or complement the one or more keywords. The tool can also be configured to generate, at least partly based on the knowledge graph a list of recommended keywords that will either replace or complement the one or two keywords used in the paid search campaign at the identified site.

In some embodiments, the software is configured to further receive one or more keywords identified on the website.

In some embodiments, a crawler may be configured to further acquire content from an identified website.

In some embodiments, an ensemble is applied to the website content.

In some embodiments, a crawler may be configured to obtain data from a plurality of web content sources, including news articles, blogs, and web sites.

In some embodiments, one or more keyphrase extraction algorithms include a Bayesian ensemble.

In some embodiments, it is configured to rank the list of recommended keywords by at least two of the following: an attractiveness score and a volume score.

In some embodiments, the software is configured to also output the enumerated keyword list ranked according to at least one relevance score.

In some embodiments, the software is configured to rank the recommended keywords by cost per click.

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