Analyzing user sentiment in social media: Implications for online marketing strategy

By Adrian Micu, Angela-Eliza Micu, Marius Geru, R. Lixăndroiu. 2017

94 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/b0500b01f449ee633c2dd6c2dc671d015764a1d1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Marketing

1. Mingshu Wang
2. Yabing Zhao
3. Xun Xu

In Sociology and Political Science

1. Mingshu Wang
2. Yabing Zhao
3. Xun Xu

Citations in the corpus

• Marketing Strategy: Pricing strategies and its influence on consumer purchasing decision (2021, background): “People could meet in groups in person, but group-based interactions through marketing, such as the telephone, were uncommon (Micu et al.2017).”
• Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews (2019, methodology): “Specifically, we implemented the Testimonial toolkit formula in the TextBlob Python library (Giatsoglou et al., 2017; Loria et al., 2014; Micu et al., 2017) and used a sentiment analysis tool in the library that uses deep learning techniques to calculate subjectivity and polarity measurements.”