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> tackle sentiment analysis VADER (Valence Aware Dictionary and sEntiment Reasoner) is a large lookup table mapping words to a sentiment score, calculated by s

> tackle sentiment analysis VADER (Valence Aware Dictionary and sEntiment Reasoner) is a large lookup table mapping words to a sentiment score, calculated by surveying people. It is simple to use and involves no ML.

PeterisP 5y ago
I don't consider VADER as suitable for tackling sentiment analysis but perhaps we have a different understanding of 'tackling sentiment analysis'. In my view, 'tackling sentiment analysis' requires doing sentiment analysis well, which no systems currently do. Decent sentiment analysis is a yet unsolved problem and current state of art solutions (mostly based on large pretrained langugage models like BERT, leveraging transfer learning from very large unlabeled corpora) are not sufficiently accurate, and of course neither is VADER. As soon as you go beyond simple binary polarity (blatantly positive/negative sentiment) and something more informative like various five-way sentiment tasks, or aspect-based sentiment or determining sentiment targets, sentiment analysis still has a long, long way to go. Current ML solutions are a possible way to tackle sentiment analysis because while the current models are not good enough, we have evidence showing that with increased model and data size they improve and perhaps might result in sufficiently good sentiment analysis someday. Perhaps not, and we'll need something else; but at least they have some potential. VADER and similar methods are not a way to tackle sentiment analysis because not only the current VADER model is not nearly good enough, it's clear that they can't scale to anything much better than that. Adding extra surveys to increase the lookup table quickly hits diminishing returns (unlike ML systems where we consistently see that ever larger models continue to get improvements), and if they can't even reach the current bar of ML models (which still are not good enough) then they can't bring us to the level of sentiment analysis where we want to be, they are a dead end. Tackling sentiment analysis also requires handling a wide variety of languages, not only English, which is yet another aspect where data-driven models have an advantage over systems that require extensive human labor for each new language. I mean, we all (me included!) want to believe that human knowledge encoded in rules can work, intuitively it's a very appealing concept, however, it does not work out in practice and IMHO in the end we all have to learn to accept the Bitter Lesson (http://incompleteideas.net/IncIdeas/BitterLesson.html http://incompleteideas.net/IncIdeas/BitterLesson.html).

PeterisP 5y ago

I don't consider VADER as suitable for tackling sentiment analysis but perhaps we have a different understanding of 'tackling sentiment analysis'. In my view, 'tackling sentiment analysis' requires doing sentiment analysis well, which no systems currently do. Decent sentiment analysis is a yet unsolved problem and current state of art solutions (mostly based on large pretrained langugage models like BERT, leveraging transfer learning from very large unlabeled corpora) are not sufficiently accurate, and of course neither is VADER. As soon as you go beyond simple binary polarity (blatantly positive/negative sentiment) and something more informative like various five-way sentiment tasks, or aspect-based sentiment or determining sentiment targets, sentiment analysis still has a long, long way to go. Current ML solutions are a possible way to tackle sentiment analysis because while the current models are not good enough, we have evidence showing that with increased model and data size they improve and perhaps might result in sufficiently good sentiment analysis someday. Perhaps not, and we'll need something else; but at least they have some potential. VADER and similar methods are not a way to tackle sentiment analysis because not only the current VADER model is not nearly good enough, it's clear that they can't scale to anything much better than that. Adding extra surveys to increase the lookup table quickly hits diminishing returns (unlike ML systems where we consistently see that ever larger models continue to get improvements), and if they can't even reach the current bar of ML models (which still are not good enough) then they can't bring us to the level of sentiment analysis where we want to be, they are a dead end. Tackling sentiment analysis also requires handling a wide variety of languages, not only English, which is yet another aspect where data-driven models have an advantage over systems that require extensive human labor for each new language. I mean, we all (me included!) want to believe that human knowledge encoded in rules can work, intuitively it's a very appealing concept, however, it does not work out in practice and IMHO in the end we all have to learn to accept the Bitter Lesson (http://incompleteideas.net/IncIdeas/BitterLesson.html http://incompleteideas.net/IncIdeas/BitterLesson.html).