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Research Paper

A natural language processing framework to analyse the opinions on HPV vaccination reflected in twitter over 10 years (2008 - 2017)

ORCID Icon, ORCID Icon & ORCID Icon
Pages 1496-1504 | Received 23 Mar 2019, Accepted 28 May 2019, Published online: 16 Jul 2019

Figures & data

Figure 1. An NLP framework for HPV tweets analysis.

Figure 1. An NLP framework for HPV tweets analysis.

Figure 2. Example of sentiment analysis.

Figure 2. Example of sentiment analysis.

Figure 3. Example of entity extraction and analysis.

Figure 3. Example of entity extraction and analysis.

Table 1. Number of tweets of each year.

Figure 4. Boxplot of sentiment scores changes over 10 years.

Figure 4. Boxplot of sentiment scores changes over 10 years.

Figure 5. (a) Phrase cloud of positive tweets. (b) Phrase cloud of negative tweets.

Figure 5. (a) Phrase cloud of positive tweets. (b) Phrase cloud of negative tweets.

Table 2. Sample positive and negative tweets contain “CDC”, “FDA”, or “MERCK”.

Figure 6. Frequency of “CDC”, “FDA” and “Merck” in the positive and negative tweets.

Figure 6. Frequency of “CDC”, “FDA” and “Merck” in the positive and negative tweets.

Table 3. Top frequent location entities in positive tweets.

Table 4. Top frequent location entities in negative tweets.

Table 5. Phrase association mining for negative tweets.

Table 6. Phrase association mining for positive tweets.

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