Sentiment Analysis of News Headlines I'm trying to do sentiment analysis of News Headlines about a particular subject mentioned in it.

Initially, I used TextBlob library for sentiment analysis to generate a polarity score. But the polarity score being generated for news headlines are not accurate. It is classifying negative news as positive news.

For example: Goldman Sachs CEO apologizes to people of Malaysia. This news is being classified as positive.

Post that, I've tried to build a custom model using spaCy library. I trained the custom model on 500 manually labelled news headlines. After training the model, I ran it on new set of headlines. The accuracy has improved but there is still scope for improvement. The accuracy is particularly poor with regard to financial news and editorial headlines.

My expectations is to create a model that can accurately predict the sentiment of news headline. So that I can plot sentiment trend about the subject over a time period.
Try to find a pattern in the sentences which are failing.
Use negspacy, find out words which are negative, replace them with opposite words. eg -
'not good' -&gt; 'bad'
try using bert transformers pretrained sentiment classification models.