Home/ML Projects - Understanding and clustering hashtags according to their word distributions using NLP in machine learning
ML Projects - Understanding and clustering hashtags according to their word distributions using NLP in machine learning
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![](https://www.facebook.com/dialog/feed?app_id=1767574003460647&link=https://pantechelearning.myinstamojo.com/product/ml-projects-understanding-and-clustering-has&picture=https://storeassets.im-cdn.com/temp/cuploads/ap-south-1%3Afe8af277-4cd5-47f4-b708-2cf09cc993b0/pantechelearning/products/1615977237803PAN-ML-014--3-.JPG&name=ML Projects - Understanding and clustering hashtags according to their word distributions using NLP in machine learning&caption=undefined&description=<p class="ql-align-justify"><span style="color: rgb(0, 0, 0); background-color: transparent;">Hashtags (single tokens often composed of natural language n-grams or abbreviations, prefixed with the character ‘#’) are ubiquitous on social networking services, particularly in short textual documents (a.k.a. posts). Authors use hashtags to diverse ends, many of which can be seen as labels for classical NLP tasks: disambiguation (chips #futurism vs. chips #junkfood); identification of named entities (#sf49ers); sentiment (#dislike); and topic annotation (#yoga). Hashtag prediction is the task of mapping text to its accompanying hashtags. In this work we propose a novel model for hashtag prediction, and show that this task is also a useful surrogate for learning good representations of text. At last we are predicting, hashtag based detailed query then we are showing the result as whether it will be positive or negative using svm and random forest algorithm.</span></p><p><br></p>&redirect_uri=http://www.facebook.com)
ML Projects - Understanding and clustering hashtags according to their word distributions using NLP in machine learning
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Hashtags (single tokens often composed of natural language n-grams or abbreviations, prefixed with the character ‘#’) are ubiquitous on social networking services, particularly in short textual documents (a
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