Abstract
Sentiment analysis (SA) is a critical aspect of social media communication as it enables the extraction and interpretation of emotions from the vast volume, speed, and diversity of user-generated content (UGC). Sentiment analysis aids in identifying the emotions of online content, distinguishing whether it is positive, negative, or neutral, and determining the author's emotion or attitude towards a particular topic. Therefore, SA is increasingly utilized in social media for business and management communication, including prediction, decision-making, and opinion tracking.
This study systematically reviews sentiment analysis in business and management communication domains from 2013-2022. Utilizing the TCCM framework (theories, constructs, characteristics, and methods), 301 articles were analyzed where research topics (i.e., public opinion, CRM, CSR, health communication), methods (lexicon-based, machine learning, hybrid approach), languages, regional attributes, emotions, and platforms were scrutinized.
The results revealed the trends in the application of sentiment analysis on social media and provide potential directions for future research. In addition to need of analyzing a more diverse range of sentiments, sentiment analysis should be applied to a wide range of languages and consider cultural differences between regions. When sampling on social media platforms, attention could be placed on the characteristics of platform users to avoid potential biases affecting the validity of sentiment detection. Furthermore, digital ethics should be discussed during sentiment analysis process, including data mining, analysis, and presentation of results. This study provides valuable insights for practical applications and deepens our understanding on the use of sentiment analysis in business and management communication.
This study systematically reviews sentiment analysis in business and management communication domains from 2013-2022. Utilizing the TCCM framework (theories, constructs, characteristics, and methods), 301 articles were analyzed where research topics (i.e., public opinion, CRM, CSR, health communication), methods (lexicon-based, machine learning, hybrid approach), languages, regional attributes, emotions, and platforms were scrutinized.
The results revealed the trends in the application of sentiment analysis on social media and provide potential directions for future research. In addition to need of analyzing a more diverse range of sentiments, sentiment analysis should be applied to a wide range of languages and consider cultural differences between regions. When sampling on social media platforms, attention could be placed on the characteristics of platform users to avoid potential biases affecting the validity of sentiment detection. Furthermore, digital ethics should be discussed during sentiment analysis process, including data mining, analysis, and presentation of results. This study provides valuable insights for practical applications and deepens our understanding on the use of sentiment analysis in business and management communication.
| Original language | English |
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| Publication status | Not published / presented only - 21 Sept 2023 |
| Event | 76th Annual Conference of the World Association for Public Opinion Research - Salzburg, Austria Duration: 19 Sept 2023 → 22 Sept 2023 |
Conference
| Conference | 76th Annual Conference of the World Association for Public Opinion Research |
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| Country/Territory | Austria |
| City | Salzburg |
| Period | 19/09/23 → 22/09/23 |