Improving sentiment scoring mechanism: a case study on airline services
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...
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| Format: | Article |
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Emerald
2018
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| _version_ | 1825721728171507712 |
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| author | Kaur, Wandeep Balakrishnan, Vimala |
| author_facet | Kaur, Wandeep Balakrishnan, Vimala |
| author_sort | Kaur, Wandeep |
| collection | UM |
| description | 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. |
| first_indexed | 2024-03-06T05:54:46Z |
| format | Article |
| id | um.eprints-21666 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:54:46Z |
| publishDate | 2018 |
| publisher | Emerald |
| record_format | dspace |
| spelling | um.eprints-216662019-07-19T08:34:21Z http://eprints.um.edu.my/21666/ Improving sentiment scoring mechanism: a case study on airline services Kaur, Wandeep Balakrishnan, Vimala QA75 Electronic computers. Computer science 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. Emerald 2018 Article PeerReviewed Kaur, Wandeep and Balakrishnan, Vimala (2018) Improving sentiment scoring mechanism: a case study on airline services. Industrial Management & Data Systems, 118 (8). pp. 1578-1596. ISSN 0263-5577, DOI https://doi.org/10.1108/IMDS-07-2017-0300 <https://doi.org/10.1108/IMDS-07-2017-0300>. https://doi.org/10.1108/IMDS-07-2017-0300 doi:10.1108/IMDS-07-2017-0300 |
| spellingShingle | QA75 Electronic computers. Computer science Kaur, Wandeep Balakrishnan, Vimala Improving sentiment scoring mechanism: a case study on airline services |
| title | Improving sentiment scoring mechanism: a case study on airline services |
| title_full | Improving sentiment scoring mechanism: a case study on airline services |
| title_fullStr | Improving sentiment scoring mechanism: a case study on airline services |
| title_full_unstemmed | Improving sentiment scoring mechanism: a case study on airline services |
| title_short | Improving sentiment scoring mechanism: a case study on airline services |
| title_sort | improving sentiment scoring mechanism a case study on airline services |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT kaurwandeep improvingsentimentscoringmechanismacasestudyonairlineservices AT balakrishnanvimala improvingsentimentscoringmechanismacasestudyonairlineservices |