**3.1. Polarity of topical issues**

A typical approach to sentiment analysis is to start with a lexicon of positive and negative words and phrases [4]. Polarity describes whether a word seems to evoke something positive

or something negative. For example, *beautiful* has a positive polarity and *horrid* has a negative polarity. Examples of tweets that represent a positive polarity include:

**Figure 2.** Sentiment polarity of tweets obtained on the decriminalization of marijuana in Jamaica.

Using Sentiment Analysis and Machine Learning Algorithms to Determine Citizens' Perceptions

http://dx.doi.org/10.5772/intechopen.72521

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**Figure 3.** Sentiment polarity of no-retweets obtained on the decriminalization of marijuana in Jamaica.

*Eg. 1:* "Jamaica legalizes medical marijuana and decriminalizes recreational use."

*Eg. 2:* "the jamaican cabinet approves a bill to legalise use of small amounts of marijuana which will be examined in the senate this week."

Examples of tweets classified as having a negative polarity include:

*Eg. 1:* "what nbc didn't show kaci fennell miss jamaica"

*Eg. 2:* "miss jamaica says the miss universe pageant "went exactly as it should""
