Latent semantic optimization is based on latent semantic indexing. Here, search engines take into account not only search results that include the searched keyword or a particular phrase, but also results that are semantically related. To this end, it analyzes semantic links between words and phrases. Thus, the content relevance of a web page can also be detected if the searched keyword was not used at all in the article in question. In the context of search engine optimization, this circumstance constitutes further opportunities for improvement in the rankings.
Latent semantic indexing makes keyword stuffing difficult for websites that are not topic-relevant. If a keyword is set in a wrong context, just so that it is listed on the website, Google recognizes the lack of semantic relevance and ranks the site as of little relevance for this search’s topic. If a website on the other hand, contains a variety of semantically related terms in addition to the main keyword that are related to the search query, it tends to be classified as more relevant than a page where this is not the case.
Latent semantic optimization is aimed specifically at the content of a webpage. Besides classical optimization on one or more keywords, which is still important, a text also needs to be semantically optimized. To this end, you specify words and phrases that are related to the topic of the text and the main keyword. Google will rank the relevance of the page higher when latent semantic indexing is used.
Tools exist to identify these semantically related words and they are mostly free of charge. The Google AdWords Keyword Planner offers the option to retrieve semantically related and similar keywords under the section “Additional keywords that should be considered.”
It is assumed that more or less semantically optimized texts will be created automatically if you write naturally and in an informative style. However, if you concentrate on an exaggerated density for a given keyword, most other synonyms will be missed. In this case, attention should be put on writing in such a way that the semantic relationship is maintained so as not to compromise the prospects of a good search engine ranking despite correct keyword density.
The TF*IDF method has a similar approach to latent semantic optimization.
In this method, the top 10 of the write naturally and in an informative style. However, if you concentrate on an exaggerated SERPs are analyzed using a predefined keyword. It filters out which terms are used in what frequency. The approach follows the assumption that the integration of semantically related terms in the correct frequency can help to improve one’s ranking, because a greater semantic proximity can be detected. Website operators that have consistently applied this system were able to achieve success. TF*IDF represents a continuation of LSO because the method precisely determine the most important terms using appropriate tools and makes a recommendation with respect to the frequency of use.