Sentiment analysis is one of the most successful and widespread applications in natural language processing. However, for all the hype it has generated since its inception, there are still many issues associated with it.
In my work with Brandtix and other startups I had the opportunity to work a lot with sentiment analysis, especially in the context of social media analytics. Doing sentiment analysis can be very easy and cheap, as there are many free libraries for that. Some examples are: Syuzhet (for R), NLTK (python), spacy (python). However, doing sentiment analysis sometimes can be very tricky and difficult and this is what I want to talk about here.
Specifically, sentiment analysis suffers from one major drawback: it is context and language specific. In this article, I am talking about some potential issues that might arise when you try to apply sentiment analysis to some domains.
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