Answers.Body: <p>It depends very much on the structure of the data.</p>
  
  <p>I would think about feature extraction first, which could be certain words occurring in the bio, and a class of user name ('real' name, numerical id, etc). Once you have a set of features for each data item, turn them into a list of feature vectors.</p>
  
  <p>Then run them through a number of machine learning algorithms. This is where the shape of the data matters, as some algorithms will work better than others. I would try eg decision trees (ID3), which are very efficient once trained (but they don't give you a confidence interval). But any other ML algorithm might work. They will all have trade-offs with speed of training, memory requirements, and speed of classification; some will give you a class-label probability, others will just give you one label.</p>
  
  <p>The best way would be to use a sample, and identify which algorithm works well and fits your specific requirements. Then use that for the full data set.</p>
  
  <p>Alternatively you could just use, for example, the <a href="https://nlp.stanford.edu/wiki/Software/Classifier" rel="nofollow noreferrer">Stanford ML classifier</a>. That will give you a confidence interval, and will probably work reasonably well.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-05-01T11:36:04.023Z
Answers.Text: Thank you so much Oliver - this is really helpful. I will try and implement as you have said
Answers.UserDisplayName: user9645302
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-05-01T15:49:16.877Z
Answers.Text: Note that selecting the right features might have a bigger impact on the outcome than the actual ML algorithm, so it's worth trying out several alternatives there as well.
Answers.UserDisplayName: Oliver Mason
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-05-01T08:37:29.457Z
Answers.LastActivityDate: 2018-05-01T08:37:29.457Z
Answers.UserDisplayName: Oliver Mason
Body: <p>I have an approximately 90,000 row dataset that has information of social media profiles which has columns for biography, follower count, language spoken, name, username and the <strong>label (to identify whether the profile is that of an influencer, brand or news and media)</strong>.</p>
  
  <p>Task: I have to train a model that predicts the label. I then need to produce a confidence interval for each prediction.</p>
  
  <p>As I have never come across a problem like this, I am just after some suggestions of what models I should be using for a situation like this? I am thinking Natural Language Processing (NLP), but not sure. </p>
  
  <p>Also, for NLP (if a suitable method), any codes or advice to help me implement for the first time on Python would be greatly appreciated! Thanks in advanced</p>
  
Tags: <training><natural-language-processing><sentiment-analysis>
Title: Recommended Modelling Technique for Influencer Marketing Scenario
UserDisplayName: user9645302
ThreadText: # Recommended Modelling Technique for Influencer Marketing Scenario
  
  (Asked by: username_0 on 2018-04-30)
  
  I have an approximately 90,000 row dataset that has information of social media profiles which has columns for biography, follower count, language spoken, name, username and the **label (to identify whether the profile is that of an influencer, brand or news and media)**.
  
  Task: I have to train a model that predicts the label. I then need to produce a confidence interval for each prediction.
  
  As I have never come across a problem like this, I am just after some suggestions of what models I should be using for a situation like this? I am thinking Natural Language Processing (NLP), but not sure.
  
  Also, for NLP (if a suitable method), any codes or advice to help me implement for the first time on Python would be greatly appreciated! Thanks in advanced
  
  **Tags:** training, natural-language-processing, sentiment-analysis
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2018-05-01. Score: 1 | Accepted answer
  
  It depends very much on the structure of the data.
  
  I would think about feature extraction first, which could be certain words occurring in the bio, and a class of user name ('real' name, numerical id, etc). Once you have a set of features for each data item, turn them into a list of feature vectors.
  
  Then run them through a number of machine learning algorithms. This is where the shape of the data matters, as some algorithms will work better than others. I would try eg decision trees (ID3), which are very efficient once trained (but they don't give you a confidence interval). But any other ML algorithm might work. They will all have trade-offs with speed of training, memory requirements, and speed of classification; some will give you a class-label probability, others will just give you one label.
  
  The best way would be to use a sample, and identify which algorithm works well and fits your specific requirements. Then use that for the full data set.
  
  Alternatively you could just use, for example, the [Stanford ML classifier](https://nlp.stanford.edu/wiki/Software/Classifier). That will give you a confidence interval, and will probably work reasonably well.
  
  > **Comments:**
  > **username_0**: Thank you so much Oliver - this is really helpful. I will try and implement as you have said  
  > **username_1**: Note that selecting the right features might have a bigger impact on the outcome than the actual ML algorithm, so it's worth trying out several alternatives there as well.  
  
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