Sentiment analysis can be implemented in a number of ways. The easiest to both implement and understand are lexicon-based approaches. These methods leverage the use of lists (lexicons) of polarized words and expressions. Given a sentence, these methods count the number of positive and negative words and expressions. If there are more positive words/expressions, the sentence is labeled as positive. If there are more negative than positive words/expressions, the sentence is labeled as negative. If the number of positive and negative words/expressions are equal, the sentence is labeled as neutral. Although this approach is relatively easy to code and does not require any training, it has two major disadvantages. First, it does not take into account interactions between words. For example, not bad, which is actually a positive expression, can be classified...
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