More than words: Social networks' text mining for consumer brand sentiments

By Mohamed M. Mostafa. 2013

593 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science, Sentiment & Opinion Analysis, Human-Computer Interaction

How researchers see this paper

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

In Artificial Intelligence

In Sociology and Political Science

In Sentiment & Opinion Analysis

In Human-Computer Interaction

Citations in the corpus

Social Biases in NLP Models as Barriers for Persons with Disabilities (2020, background): “This is important because NLP models are increasingly being used for tasks such as fighting online abuse (Jigsaw, 2017), measuring brand sentiment (Mostafa, 2013), and matching job applicants to job opportunities (De-Arteaga et al.”
Exploring online customer engagement with hospitality products and its relationship with involvement, emotional states, experience and brand advocacy (2019, methodology): “This tool also allows users themselves to define the polarity of entities and concepts in the study, which makes this tool suitable for any type of research (Manning et al., 2014; Mostafa, 2013).”
Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment (2015, methodology): “It can be used in areas of research like emotion detection in suicide notes as done by Desmet and Hoste (2013); or it can be used, for example, to determine consumer sentiment towards a brand (Mostafa, 2013).”
Text mining for market prediction: A systematic review (2014, background): “It deals with detecting the emotional sentiment preserved in text through specialized semantic analysis for a variety of purposes for instance to gauge the quality of market reception for a new product and the overall customer feedback or to estimate the popularity of a product or brand (Ghiassi, Sk”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, background): “S# Tasks and applications #Articles References 1 Subjectivity Classification 6 [44], [75], [110], [163], [167], [174] 2 Polarity determination 43 [12], [26], [29], [32], [33], [35], [40], [45], [48], [50], [54], [57], [66], [85], [95], [96], [108], [109], [112], [114], [123], [126], [154], [156], [1”