Gehandicapten dating site, alexa stats for gehandicaptendating.com
When using all user tweets, they reached an accuracy of Although LP performs worse than it could on fixed numbers of principal components, its more detailed confidence score allows a better hyperparameter selection, on average selecting around 9 principal components, where TiMBL chooses a wide range of numbers, and generally far lower than is optimal.
The creators themselves used it for various classification tasks, including gender recognition Koppel et al. 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.
Again, we decided to explore more than one option, but here we preferred more focus and restricted ourselves to three systems.
Figures 1, 2, and 3 show accuracy measurements for the token unigrams, token bigrams, and normalized character 5-grams, for all three systems at various numbers of Gehandicapten dating site components. However, we do observe different behaviour when reversing the signs.
Top rankingfemales insvr ontokenunigrams, with ranksand scoresforsvr with Dating agency nz feature types. In fact, for all the tokens n-grams, it would seem that the further one goes away from the unigrams, the worse the accuracy gets.
Experimental Data and Evaluation In this section, we first describe the corpus that we used in our experiments Section 3.
And by TweetGenie as well. For each blogger, metadata is present, including the blogger s self-provided gender, age, industry and astrological sign. Even the character 5-grams have ranks up to 40 for this top Roughly speaking, it classifies on the basis of noticeable over- and underuse of specific features.
There is much more variation in the topics, but most of it is clearly girl talk of the type described in Section 5. Taking again SVR on unigrams as our starting point, this group contains 11 males and 16 females.
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However, our starting point will always be SVR with token unigrams, this being the best performing combination.
We checked gender manually for all selected users, mostly on the basis 3. As we approached the task from a machine learning viewpoint, we needed to select text features to be provided as input to the machine learning systems, as well as machine learning systems which are to use this input for classification.
Unigrams are mostly closely mirrored by the character 5-grams, as could already be suspected from the content of these two feature types. On the female side, everything is less extreme.
In this case, the Twitter profiles of the authors are available, but these consist of freeform text rather than fixed information fields.
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A model, called profile, is constructed for each individual class, and the system determines for each author to which degree they are similar to the class profile. We will only look at the final scores for each combination, and forgo the extra detail of any underlying separate male and female model scores which we have for SVR and LP; see above.
Our primary choice for classification was the use of Support Vector Machines, viz. We selected of these so that they get a gender assignment in TwiQS, for comparison, but we also wanted to include unmarked users in case these would be different in nature. This is in accordance with the hypothesis just suggested for the token n-grams, as normalization too brings the character n-grams closer to token unigrams.
Normalized 3-gram About 36K features. In this section, we want to investigate how strong this dependency may have been. Confidence scores for gender assignment with regard to the female and male profiles built by SVR on the basis of token unigrams.
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We then measured for which percentage of the authors in the corpus this score was in agreement with the actual gender. However, all systems are in principle able to reach the same quality i.
Assuming that any sequence including periods is likely to be a URL provesunwise, given that spacing between normal wordsis often irregular.
These statistics are derived from the users profile information by way of some heuristics. 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.
For those techniques where hyperparameters need to be selected, we used a leave-one-out strategy on the test material. If no cue is found in a user s Gehandicapten dating site, no gender is assigned.
In this section, we will attempt to get closer to the answer to this question. If we search for the word parlement parliament in our corpus, which is used 40 times by Sargentini, we find two more female authors each using it onceas compared to 21 male authors with up to 9 uses.
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.
Trigrams Three adjacent tokens.
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