Can I use the training rows multiple times while training with different labels attached to it? If I have a data set where for every text message,  same but two labels are given. It could be that only one label has been filled. To visualise this scenario in real life, one may classify the accent to belong to 'US English' and 'UK English'.  

Here is an example where fro every text, atmost 2 sentiments' values are given.     


text - Sentiment - sentiment 


Ron is very amicable person - 5 - 4
That is so not the place to enjoy - 1
Rita is not so caring - 1 - 2



Now, I have to predict SINGLE sentiment column for the given text column. 

Can I create the training dataset as below for training purpose? What could be difficulties in doing so?  


text - Sentiment 


Ron is very amicable person - 5
Ron is very amicable person - 4
That is so not the place to enjoy - 1
Rita is not so caring - 1
Rita is not so caring - 2



Edit:
My focus is not on choosing a single class for given text but on the decision to include that text two times with different class attributed to it.
The problem you are describing is a perfect use case for Fuzzy Logic.

Contrary to boolean logic where things are either black or white, fuzzy logic allows things to be grey.

Take the example of weather. Given a temperature, some people might say it is cold whereas other would say it is warm. There will be situations where all say it is cold, but there exists a mid-term where the classification isn't $\{0,1\}$ but rather $[0,1]$.



Therefore, it won't be a problem given that your chosen algorithm allows for probabilistic output.

So given the temperature example. You have the classes [cold, warm, hot].
If your algorithm can only output binary classifications (e.g. [0, 1, 0]), then better only input data points with only 1 label. However, if your algorithm can output probabilistic classifications (e.g. [0.2, 0.7, 0.1]) then you can input the same datapoint with multiple labels.

In the end, you can pick the class with the largest probability, but you need to understand that this might not give the output you are expecting. If you get a classification of [0.49, 0.51, 0.0], you might say is class 2 where in truth the model is not actually telling you that.