<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>

CC BY-SA 3.0
2018-05-01T11:36:04.023Z
Thank you so much Oliver - this is really helpful. I will try and implement as you have said
user9645302
CC BY-SA 3.0
2018-05-01T15:49:16.877Z
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.
Oliver Mason
CC BY-SA 3.0
2018-05-01T08:37:29.457Z
2018-05-01T08:37:29.457Z
Oliver Mason
<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>

<training><natural-language-processing><sentiment-analysis>
Recommended Modelling Technique for Influencer Marketing Scenario
user9645302
# 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


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## 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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