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Fig. 5. Sentiment scores for Nokia (top) and Pfizer (bottom). X-axis represents score distributions, Y-axis represents count/frequencies.

Figure 5 Sentiment scores for Nokia (top) and Pfizer (bottom). X-axis represents score distributions, Y-axis represents count/frequencies.

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Brands included in the study and their average sentiment scores. Table 1 on linguistic aspects of comments such as vocabulary, sentence length and part-of-speech distribution. The authors found that comments on discussion boards and user reviews contain more verbs and adverbs compared to the heavy usage of nouns and prep- ositions found in bloggers and critic postings. The study also iden- tified the most frequent positive and negative terms used across different genres along with the distribution patterns of such terms. 2.3. Political orientation
Brands included in the study and their average sentiment scores. Table 1 on linguistic aspects of comments such as vocabulary, sentence length and part-of-speech distribution. The authors found that comments on discussion boards and user reviews contain more verbs and adverbs compared to the heavy usage of nouns and prep- ositions found in bloggers and critic postings. The study also iden- tified the most frequent positive and negative terms used across different genres along with the distribution patterns of such terms. 2.3. Political orientation
Fig. 2. Top 20 countries in Twitter accounts as of January 1, 2012 (source: Semiocast.com).
Fig. 2. Top 20 countries in Twitter accounts as of January 1, 2012 (source: Semiocast.com).
Sample of tweets for Air India.
Sample of tweets for Air India.
Word frequencies of a sample of tweets after removing stopping and unique words.
Word frequencies of a sample of tweets after removing stopping and unique words.
Fig. 4. A 3-D map with link strengths and base lines shown.
Fig. 4. A 3-D map with link strengths and base lines shown.
Fig. 6. Sentiment analysis for a random tweets sample-after eliminating neutral tweets-for Lufthansa (top), DHL (middle) and T-Mobile (bottom) brands
Fig. 6. Sentiment analysis for a random tweets sample-after eliminating neutral tweets-for Lufthansa (top), DHL (middle) and T-Mobile (bottom) brands
Fig. 7. Twitter stream graph (1000 tweets each) for Nokia (top), Pfizer (middle) and Al-Jazeera English brands (bottom).
Fig. 7. Twitter stream graph (1000 tweets each) for Nokia (top), Pfizer (middle) and Al-Jazeera English brands (bottom).
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