Summing three lexicon based approach methods for sentiment analysis? I'm doing sentiment analysis using a lexicon based approach and I have a bunch of news headlines that needs to be categorized as negative, positive and neutral or within a scale ranging from -1 (very negative sentiment) to +1 (very positive sentiment).
I'm considering using three different methods: TextBlob, Vader (Valence Aware Dictionary and Sentiment Reasoner) and LIWC2015 as I have access to it. This would lead to three predicted sentiment of each headline.
I hove two question:

Which are the pros and cons of this technique?
And, once runned the 3 analisys, how can I melt all them into a single metrics? Is standardize/normalize the 3 metrics and averaging them a good solution?
What you're proposing is some very simple form of ensemble learning.
You need to have at least a sample of labelled data in order to evaluate any method. Using this labelled data you can:

evaluate each of the three methods on their own
evaluate your idea of averaging the 3 methods predictions
if you have enough labelled data, you could even train a model which combines their predictions optimally (this would be full-fledged stacked generalization)