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Purpose The purpose of this paper is to investigate the effect of including letter repetition commonly found within social media text and its impact in determining the sentiment scores for two major airlines in Malaysia. Design/methodology/approach A Sentiment Intensity Calculator (SentI-Cal) was developed by assigning individual weights to each letter repetition, and tested it using data collected from official Facebook pages of the airlines. Findings Evaluation metrics indicate that SentI-Cal outperforms the baseline tool Semantic Orientation Calculator (SO-CAL), with an accuracy of 90.7 percent compared to 58.33 percent for SO-CAL. Practical implications A more accurate sentiment score allows airline services to easily obtain a better understanding of the sentiments of their customers, hence providing opportunities in improving their airline services. Originality/value Proposed mechanism calculates sentiment intensity of social media text by assigning individual weightage to each repeated letter and exclamation mark thus producing a more accurate sentiment score.

This paper compared the performance of emotion detection mechanisms using dataset crawled from Facebook diabetes support group pages. To be specific, string-based Multinomial Naïve Bayes algorithm, NRC Emotion Lexicon (Emolex) and Indico API were used to detect five emotions present in 2475 Facebook posts, namely, fear, joy, sad, anger and surprise. Both accuracy and F-score measures were used to assess the effectiveness of the algorithms in detecting the emotions. Findings indicate string-based Multinomial Naïve Bayes to outperform both Emolex (i.e. 82% vs. 78%) and Indico API (i.e. 82% vs. 50%). Further analysis also revealed emotions such as joy, fear and sadness to be of the highest frequencies for the diabetes community. Implications of the findings and emotions detected are further discussed in this paper.

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Students are the golden commodity when it comes to ringing in revenue for academic institution. Therefore, it is vital to ensure the opinions and feedback of students is taken seriously to ensure continuous improvement in the teaching and learning experience. In an era of digital information and rich opinionated text being easily available, it is crucial to look into different forms of data analysis from which vital information can be extracted. Sentiment and emotion analysis are one such area of research that looks to extract implicit information from written text and analyse data that would be able to provide a deeper insight compared to conventional measures. This paper is an extension of a previously conducted study of analysing emotion as well as sentiment of students’ feedback taking Thermal Engineering (MEC551) from Universiti Teknologi Mara (UiTM). Supervised learning technique was adopted and data analysed revealed students were biased towards assignment and quizzes as these would help improve their carry forwards for the subject and the preference of chapters to the exam was more for conduction and convection compared to others which had more mathematical related calculation.