SMS tracker for iPhone without jailbreak now. Today it is easy to spy on others WhatsApp.
We achieved the best results, However, as any collection that is harvested automatically, its usability is reduced by a lack of reliable metadata.
In this case, the Twitter profiles of the authors are available, but these consist of freeform text rather than fixed information fields.
And, obviously, it is unknown to which degree the information that is present is true. The resource would become even more useful if we could deduce complete and correct metadata from the various available information sources, such as the provided metadata, user relations, profile photos, and the text of the tweets.
In this paper, we start modestly, by attempting to derive just the gender of the authors 1 automatically, purely on the basis of the content of their tweets, using author profiling techniques. For our experiment, we selected authors for whom we were able to determine with a high degree of certainty a that they were human individuals and b what gender they were.
We also varied the recognition features provided to the techniques, using both character and token n-grams. For all techniques and features, we ran the same 5-fold cross-validation experiments in order to determine how well they could be used to distinguish between male and female authors of tweets.
In the following sections, we first present some previous work on gender recognition Section 2. Then we describe our experimental data and the evaluation method Section 3after which we proceed to describe the various author profiling strategies that we investigated Section 4.
Then follow the results Section 5and Section 6 concludes the paper. For whom we already know that they are an individual person rather than, say, a husband and wife couple or a board of editors for an official Twitterfeed. Gender Recognition Gender recognition is a subtask in the general field of authorship recognition and profiling, which has reached maturity in the last decades for an overview, see e.
Juola and Koppel et al. Currently the field is getting an impulse for further development now that vast data sets of user generated data is becoming available. Even so, there are circumstances where outright recognition is not an option, but where one must be content with profiling, i.
In this paper we restrict ourselves to gender recognition, and it is also this aspect we will discuss further in this section. A group which is very active in studying gender recognition among other traits on the basis of text is that around Moshe Koppel.
In Koppel et al. For each blogger, metadata is present, including the blogger s self-provided gender, age, industry and astrological sign.
This corpus has been used extensively since. The creators themselves used it for various classification tasks, including gender recognition Koppel et al. They report an overall accuracy of Slightly more information seems to be coming from content However, even style appears to mirror content.
We see the women focusing on personal matters, leading to important content words like love and boyfriend, and important style words like I and other personal pronouns. The men, on the other hand, seem to be more interested in computers, leading to important content words like software and game, and correspondingly more determiners and prepositions.
One gets the impression that gender recognition is more sociological than linguistic, showing what women and men were blogging about back in A later study Goswami et al.
The authors do not report the set of slang words, but the non-dictionary words appear to be more related to style than to content, showing that purely linguistic behaviour can contribute information for gender recognition as well.
Gender recognition has also already been applied to Tweets. With lexical N-grams, they reached an accuracy of Their highest score when using just text features was Although LIWC appears a very interesting addition, it hardly adds anything to the classification.
With only token unigrams, the recognition accuracy was They used lexical features, and present a very good breakdown of various word types. When using all user tweets, they reached an accuracy of An interesting observation is that there is a clear class of misclassified users who have a majority of opposite gender users in their social network.
When adding more information sources, such as profile fields, they reach an accuracy of These statistics are derived from the users profile information by way of some heuristics. For gender, the system checks the profile for about common male and common female first names, as well as for gender related words, such as father, mother, wife and husband.
Full text of "The India directory, or, Directions for sailing to and from the East Indies, China, Australia, and the interjacent ports of Africa and South America: comp.
chiefly from original journals of the honourable company's ships, and from observations and remarks, resulting from the experience of twenty-one years in the navigation of those seas".
A business plan is a written description of your business's future, a document that tells what you plan to do and how you plan to do it.
If you jot down a . SMS Tracker for Nokia Download Gps; Best Way to track my girlfriends Phone! Here is the Ways to Track Messages Free! Almeria | Spain Almeria | Spain.