This approach relies on manually crafted rules for data classification to determine sentiment. This approach use dictionaries of words with positive or negative values to denote their polarity and sentiment strength to calculate a score. Additional functionality can also be added by including expressions. Rule based sentiment analysis algorithms can be customized based on context by developing even smarter rules. How it works: It counts the number of positive and negative words in the given text. If the number of positives is more than the negatives, it will return a positive sentiment. If both are equal, it will return a neutral sentiment. Disadvantages:The downside of this approach is that it does not take into account how the words are combined in a sentence, it only looks at occurrences. It is quick to implement but the model involves a long-term cost outlay as it requires regular maintenance so that you get consistent and improved results.