<p>Papers on analyzing deep learning models for sentiment analysis (<a href="https://arxiv.org/pdf/1809.08037.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1809.08037.pdf</a>, <a href="https://arxiv.org/pdf/1907.04613.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1907.04613.pdf</a>) show that the networks really learn domain-specific information.</p>

<p>For instance, if you train a model on the IMDB dataset, name <em>Tarantino</em> is always associated with positive sentiment. If you say: "It was like Tarantino." the model classifies it positively, although really should not in the political domain.</p>

<p>If you have time to play around with it, you try to gather as many sentiment datasets as you can and try to train simple classifier over BERT and see if it works.</p>

<p>If you need a quick solution, you can still do some pre-machine learning approach and rely on a sentiment lexicon. It works surprisingly well.</p>
Sentiment Analysis:using a dataset (IMDB reviews) to train a neural-net and using it to predict entirely different datasets (Political articles)
<p>We need to analyse a lot of articles relevant to political instability in a given country (things like the possibility of a coalition / a snap election etc). 
The problem is that I could not find any labeled datasets which could be plugged into a neural network (CNN/LSTM in TensorFlow) so as to supervise it for real-time events (news articles, tweets etc).</p>

<p>I believe we can't use publicly available big datasets - like IMDB film reviews - for training the models to accurately identify and predict the occurrence of such events (or can we?).</p>

<p>Are there other ways to solve this problem?</p>

<p>I also thought of using unsupervised learning - libraries like VADER - but that gives me a more generic sentiment-score, rather than attuned to the specific corpora relevant to the problem.</p>