Internet, particularly the World Wide Web (WWW) is a tremendous source of information for a variety of topics (e.g., sports, science, culture, politics, economics, psychology, law, industry, weather, etc.). Frequently, a user browses one or more pieces of information about an interested topic and the browsed information is stored eventually in the browser history. In conventional systems and methods, techniques have been described for aggregating browser histories into each topic associated with the browsed information and displaying the same as a topic view in the browser history.
Further, techniques have been described for socially augmented browsing of a website in an internet application, where the user wishes to join one or more groups of users with a similar interest (such as football). In response, the internet application may present the user with information about the Internet behavior of the users in the joined group, such as information about web site pages that the users in the joined group have viewed and products that the users in the joined group have purchased; however, the ability to provide a topic group (i.e., topic view) instantly by analyzing the information browsed by the users is limited in its ability to provide useful and actionable information.
Deriving a plurality of topics from the content is a key feature of a network entity. Conventionally, a semantic tagging method that outputs semantically linked tags for text content has been described. Methods according to this conventional aspect include inputting the text content, extracting nouns and noun phrases from the text content, detecting tokens (words) from the detected sentences, labeling the tokens, and extracting consecutive noun tokens and noting their frequency of use, mapping the extracted nouns and noun phrases to terms of an ontology, mapping the extracted nouns and noun phrases to a correct sense of the ontology terms using lexical chaining Word Sense Disambiguation (WSD) algorithms, weighting the significance of the concepts of the extracted nouns and noun phrases from their ontological and statistical features, extracting key-phrases from the weighted concepts, and outputting the key-phrases as semantic tags are well known methods. However, the ability to analyze the text and extract the top token with a section by section index of word frequency; meta data considering location, time range, validity, sentiment, category, and publication domain/house; and a topic distribution vector has limits in providing accurate information about topics browsed by the users.
The limitations discussed above provide the motivation for an improved topic view provision method and system.