*3.1.2.1. No-retweets*

The graph above depicts the results obtained from analysis of tweets that were not retweeted, as in these tweets were posted by the original author. **Figure 5** shows that seven hundred and ninety (790) tweets were negative and three hundred and ninety nine (399) were positive.

*3.1.2.2. Retweets*

being positive.

*3.1.3.1. No-retweets*

*3.1.3. Riverton Landfill fire*

tive and two hundred and forty (240) were positive.

Among retweets for the topic, one thousand five hundred and thirty (1530) tweets were nega-

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

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

71

**Figure 5.** Sentiment polarity of no-retweets obtained on Kaci Fennell's placing in Miss Universe.

**Figure 6** shows five hundred and fifty four (554) tweets of the one thousand four hundred and seven (1407) tweets collected on the Riverton Landfill fire in the Riverton community were negative. Smoke penetration from the fire was observed within a 20 mile radius from the landfill and further at times based on the wind direction. The fire lasted for 2 weeks and at least 29 critical air pollutants was detected [5]. The tweets classified as negative were Jamaicans expressing their anger toward the maintenance of the landfill and the effects of the fire on nearby communities. Six hundred and thirty three (633) tweets were classified as

The graph above depicts the results obtained from analysis of tweets that were not retweeted, as in these tweets were posted by the original author. **Figure 7** shows that two hundred and

forty five (245) tweets were negative and three hundred and five (305) were positive.

**Figure 4.** Sentiment polarity of tweets obtained on Kaci Fennell's placing in Miss Universe.

Using Sentiment Analysis and Machine Learning Algorithms to Determine Citizens' Perceptions http://dx.doi.org/10.5772/intechopen.72521 71

**Figure 5.** Sentiment polarity of no-retweets obtained on Kaci Fennell's placing in Miss Universe.
