We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to .
To save content items to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Find out more about the Kindle Personal Document Service.
In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networking websites is inherently unstructured and fuzzy in nature. In everyday life conversations, people do not care about the spellings and accurate grammatical construction of a sentence that may lead to different types of ambiguities, such as lexical, syntactic, and semantic. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several surveys have been conducted to analyze different methods for the information extraction. Most of the surveys emphasized on the application of different text mining techniques for unstructured data sets reside in the form of text documents, but do not specifically target the data sets in social networking website. This survey attempts to provide a thorough understanding of different text mining techniques as well as the application of these techniques in the social networking websites. This survey investigates the recent advancement in the field of text analysis and covers two basic approaches of text mining, such as classification and clustering that are widely used for the exploration of the unstructured text available on the Web.
Excavations conducted since Chomko's initial discovery in 1974 of Cucurbita pepo seeds have clarified their stratigraphic and radiometric context as well as delineated an earlier archaeological unit, the Squash and Gourd Zone, where a second cucurbit, Lagenaria siceraria, was found. The two units are Late Archaic with dates (weighted averages of radiocarbon assays) of 4257 ± 39 and 3928 ± 41 radiocarbon years B.P., respectively, and are beneath stratigraphically superior Late Archaic and Woodland units also containing cucurbits. A comparison of the early Cucurbita pepo with others from later contexts demonstrates an increasing size with time and morphology similar between the early seeds and the historic cultivar "Mandan." Nutritional value of the cucurbits, both cultigens, may have been comparable to that of other wild plant foods consumed. In any event, the cucurbits are artifacts of regional exchange mechanisms operating some 4000 years ago; the most plausible mechanism being down-the-line exchange.
Email your librarian or administrator to recommend adding this to your organisation's collection.