Sentiment and Emotion on Twitter: The Case of the Global Consumer Electronics Industry
Abstract
:1. Introduction
2. Review of Literature
2.1. Social Networks
2.2. Sentiment Analysis
Sentiment Analysis Emotions
3. Methodology
Data
- SnowballC created by Milan Bouchet-Valat (2020) aims to extract the words and analyze the text, it should be noted that this package includes different types of functions to use.
- The tm package (text mining package) is another tool used for data mining, similar to SnowballC, it includes different types of functions to use.
- The TwitterR package helps us to find all the information of a twitter user, such as the users, tweets, time of the tweet, and how many liked tweets. Similar to the previous packages, this one has its own functions to use.
- The Syuzhet package is responsible for extracting the sentiments and emotions from the text filtered by the previous packages.
- The text in the original tweet issued by the company.
- If the company has favored its own tweet.
- How many favorites that a particular tweet has had.
- The exact date the tweet was created, which gives us information not only about the date the tweet was issued but also about the time it was issued.
4. Research Findings
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Name of the Companies | Name in Twitter |
LG Electronics | @LGUS |
Huawei | @Huawei |
Samsung Electronics | @Samsung |
Sony | @Sony |
Xiaomi | @Xiaomi |
Motorola | @Moto |
HP | @HP |
ASUS | @ASUS |
Nokia | @nokia |
Microsoft | @Microsoft |
Dell | @Dell |
Lenovo | @Lenovo |
Intel | @intel |
AMD | @AMD |
Amazon | @amazon |
NVIDIA | @nvidia |
Logitech | @logitech |
Canon USA Corp. | @canonusa |
TCL USA | @TCL_USA |
Toshiba | @toshibausa |
OnePlus | @oneplus |
Philips | @philips |
Nintendo of America | @nintendoamerica |
NikonUSA | @nikonusa |
Bose | @bose |
Kodak | @kodak |
Panasonic Corp. | @panasonic |
Hitachi | @hitachiglobal |
Pebble | @pebble |
Source: Own elaboration. |
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Emotion | Total |
---|---|
Anger Anticipation | 117 |
Disgust | 796 |
Fear | 62 |
Joy | 225 |
Sadness | 523 |
Surprise | 119 |
Trust | 226 |
Negative | 817 |
Positive | 209 |
1738 |
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Pezoa-Fuentes, C.; García-Rivera, D.; Matamoros-Rojas, S. Sentiment and Emotion on Twitter: The Case of the Global Consumer Electronics Industry. J. Theor. Appl. Electron. Commer. Res. 2023, 18, 765-776. https://doi.org/10.3390/jtaer18020039
Pezoa-Fuentes C, García-Rivera D, Matamoros-Rojas S. Sentiment and Emotion on Twitter: The Case of the Global Consumer Electronics Industry. Journal of Theoretical and Applied Electronic Commerce Research. 2023; 18(2):765-776. https://doi.org/10.3390/jtaer18020039
Chicago/Turabian StylePezoa-Fuentes, Claudia, Danilo García-Rivera, and Sebastián Matamoros-Rojas. 2023. "Sentiment and Emotion on Twitter: The Case of the Global Consumer Electronics Industry" Journal of Theoretical and Applied Electronic Commerce Research 18, no. 2: 765-776. https://doi.org/10.3390/jtaer18020039
APA StylePezoa-Fuentes, C., García-Rivera, D., & Matamoros-Rojas, S. (2023). Sentiment and Emotion on Twitter: The Case of the Global Consumer Electronics Industry. Journal of Theoretical and Applied Electronic Commerce Research, 18(2), 765-776. https://doi.org/10.3390/jtaer18020039