Quora allows users to explicitly post anonymous questions. It allows users to follow topics, share knowledge or expertise, ask questions, and provide answers and opinions. However, after some playing around you'll find out how to follow some topics, and read some questions and answers, or even ask some of your own. We, therefore, restrict the data to these topics, which are used for computing the features. In comparison to the data sample with no categorization, this stratification of the topics enhances the prediction accuracies for several categories. We reported the best models in terms of insincerity prediction accuracy. 39% in terms of accuracy. Moreover, we analyzed the cognitive efforts users made in writing their posts and whether that can improve the prediction accuracy. In the prediction task, we remove all the questions posted by the anonymous users. Social voting helps users identify. To overcome this problem, this paper proposes a multi-layer convolutional neural network model that helps to minimize Insincere questions from the website.
Our final finding was that a simple Continuous Bag of Words neural network model had the best performance, outdoing more complicated recurrent and attention based models. For feature extraction, we used Bag of Words including Count Vectorizer, and Term Frequency-Inverse Document Frequency with unigram for XGBoost and CatBoost. We adapt the feature set which are appropriate for the topic merge and compare the performance. If you run through this process just one time, you’ll be set for good! Now that you have set up your campaign, it’s time to create your ad sets! We believe that this thorough measurement study will have a direct application to a service like recommending trending topics in Quora. But many questions have been asked and answered already, so we can judge how well the service is doing so far. We’ve put together this guide to help you know where to go to get your questions answered.
However, many question posts on this Q and A site often do not get answered. You can get answers on just about any topic you can imagine, some of them very specific and detailed. From its name, you can already tell the difference between this platform and Quora - it mainly features experts. In January 2013, a blogging platform was launched by Quora. Quora is a growing QA platform where users get quick answers to their questions from their peers. Quora is a fast growing social Q&A site where users create and answer questions, and identify the best answers by upvotes and downvotes with crowd wisdom. Scour similar questions on social networks to find an answer? Therefore, it is better to consult several sources to find the best, most accurate and up-to-date answers. Our deep learning models achieved better accuracy than machine learning models. Xgboost model with character level term frequency and inverse term frequency is our best machine learning model that has also outperformed a few of the Deep learning baseline models. We applied deep learning techniques to model four different deep neural networks of multiple layers consisting of Glove embeddings, Long Short Term Memory, Convolution, Max pooling, Dense, Batch Normalization, Activation functions, and model merge. Article h as been c re ated with G SA Conte nt Generator Demoversion!
In this paper, we consider a massive dataset of more than four years and analyze the dynamics of topical growth over time; how various factors affect the popularity of a topic or its acceptance in Q&A community. This is just a general subforum for answers, while there are many others which are more specific. Is there a prescribed etiquette for talking about the stuff we watch? Keep reading to learn about five of the best Quora alternatives out there. According to Robert Scoble, Quora is a website that has succeeded in its attempt to combine the best attributes of Facebook, Google Wave and Twitter, among others. Spaces is similar to subreddits on the Reddit website. We have tried pinging Quora website using our server. By using feature engineering, feature importance techniques, and experimenting with seven selected machine learning classifiers, we demonstrated that our models outperformed previous studies on this task. As we've mentioned, it's an excellent way to get new ideas for services - and Gig extras - from potential customers by using their questions as a starting point.