Abstract: Methods, apparatus, and articles of manufacture to rank a web site are disclosed herein. An example method to rank the influence of a web site includes accessing a query based on a topic term, determining topic specific posts by applying the query to a first plurality of posts to a web site, comparing the topic specific posts to a list of web sites to determine a topic volume per site, determining a plurality of inbound links to the first plurality of posts, determining a number of inbound links to the posts that are relevant to the topic term by comparing the topic volume per site and the inbound links, and generating a rank of the web site based on the number of inbound links to the posts that are relevant to the topic term and times between the inbound links.

Abstract: Methods, apparatus, and articles of manufacture to rank a web site are disclosed herein. An example method to rank the influence of a web site includes accessing a query based on a topic term, determining topic specific posts by applying the query to a first plurality of posts to a web site, comparing the topic specific posts to a list of web sites to determine a topic volume per site, determining a plurality of inbound links to the first plurality of posts, determining a number of inbound links to the posts that are relevant to the topic term by comparing the topic volume per site and the inbound links, and generating a rank of the web site based on the number of inbound links to the posts that are relevant to the topic term and times between the inbound links.

Abstract: Methods, apparatus, and articles of manufacture to rank a web site are disclosed herein. An example method to rank the influence of a web site includes accessing a query based on a topic term, determining topic specific posts by applying the query to a first plurality of posts to a web site, comparing the topic specific posts to a list of web sites to determine a topic volume per site, determining a plurality of inbound links to the first plurality of posts, determining a number of inbound links to the posts that are relevant to the topic term by comparing the topic volume per site and the inbound links, and generating a rank of the web site based on the number of inbound links to the posts that are relevant to the topic term and times between the inbound links.

Abstract: Methods and apparatus to classify text communications are disclosed. An example method includes determining a first score indicating a likelihood that a text belongs to a first classification mode by combining a first sentence score and a second sentence score retrieved from an index, the first sentence score indicating a probability that a first sentence in the text belongs to the first classification mode, the second sentence score indicating that a second sentence following the first sentence belongs to the first classification mode, determining a second score indicating a likelihood that the text belongs to a second classification mode, comparing the first score to the second score, classifying the text as the first classification mode when the first score is greater than the second score, and determining a confidence level that the text belongs to the first classification mode by dividing the first score by the second score.

Abstract: Methods, apparatus, and articles of manufacture to rank a web site are disclosed herein. An example method to rank the influence of a web site includes accessing a query based on a topic term, determining topic specific posts by applying the query to a first plurality of posts to a web site, comparing the topic specific posts to a list of web sites to determine a topic volume per site, determining a plurality of inbound links to the first plurality of posts, determining a number of inbound links to the posts that are relevant to the topic term by comparing the topic volume per site and the inbound links, and generating a rank of the web site based on the number of inbound links to the posts that are relevant to the topic term and times between the inbound links.

Abstract: Methods, apparatus, and articles of manufacture to rank a web site are disclosed herein. An example method to rank the influence of a web site involves receiving a topic term, filtering out a post that is not relevant to the topic term from a first plurality of posts to a web site, determining a number of inbound links to the web site based on the topic term, determining a time between the inbound links to the web site based on the topic term, and generating a rank of the web site based on the number of inbound links and the time between the inbound links.

Abstract: Methods and apparatus to classify text communications are disclosed. An example method includes determining a first conditional probability of a first feature occurring in a text given that the text belongs to a classification mode, wherein the first feature is included in the text, determining a second conditional probability of a second feature occurring in a text given that the text belongs to the classification mode, wherein the second feature is included in the text, determining a probability of the classification mode occurring, multiplying the first conditional probability, the second conditional probability and the probability of the classification mode to determine a product, and storing the product in a tangible memory as a score that the message belongs to the first classification mode.

Abstract: Methods and apparatus to classify text communications are disclosed. An example method includes determining a first score indicating a likelihood that a text belongs to a first classification mode by combining a first sentence score and a second sentence score retrieved from an index, the first sentence score indicating a probability that a first sentence in the text belongs to the first classification mode, the second sentence score indicating that a second sentence following the first sentence belongs to the first classification mode, determining a second score indicating a likelihood that the text belongs to a second classification mode, comparing the first score to the second score, classifying the text as the first classification mode when the first score is greater than the second score, and determining a confidence level that the text belongs to the first classification mode by dividing the first score by the second score.

Abstract: Methods and apparatus to classify text communications are disclosed. An example method includes determining a first conditional probability of a first feature occurring in a text given that the text belongs to a classification mode, wherein the first feature is included in the text, determining a second conditional probability of a second feature occurring in a text given that the text belongs to the classification mode, wherein the second feature is included in the text, determining a probability of the classification mode occurring, multiplying the first conditional probability, the second conditional probability and the probability of the classification mode to determine a product, and storing the product in a tangible memory as a score that the message belongs to the first classification mode.

Abstract: Methods, apparatus, and articles of manufacture to rank a web site are disclosed herein. An example method to rank the influence of a web site involves receiving a topic term, filtering out a post that is not relevant to the topic term from a first plurality of posts to a web site, determining a number of inbound links to the web site based on the topic term, determining a time between the inbound links to the web site based on the topic term, and generating a rank of the web site based on the number of inbound links and the time between the inbound links.

Abstract: Methods and apparatus to classify text communications are disclosed. An example method includes determining a first conditional probability of a first feature occurring in a text given that the text belongs to a classification mode, wherein the first feature is included in the text, determining a second conditional probability of a second feature occurring in a text given that the text belongs to the classification mode, wherein the second feature is included in the text, determining a probability of the classification mode occurring, multiplying the first conditional probability, the second conditional probability and the probability of the classification mode to determine a product, and storing the product in a tangible memory as a score that the message belongs to the first classification mode.