Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network
Section snippets
Background
Twitter offers a unique dataset in the world of brand sentiment. Public figures and brands receive sentiment messages directly from consumers in real time in a public forum. Both the targeted and competing brands have the opportunity to dissect these messages to determine changes in consumer sentiment. Taking advantage of this data, however, requires researchers to deal with analyzing an immense amount of data produced by Twitter each day, referred to as the Twitter fire hose. As noted by
Twitter sentiment analysis literature review and modeling approach
Twitter is a popular and rapidly growing computer-mediated communication platform. Twitter users create micro-blog status update messages called tweets to communicate with other users for various reasons on a wide variety of topics. These tweets often contain valuable information and the perspectives and opinions of users on issues related to business and society (Gleason, 2013, Jansen et al., 2009). Researchers have developed various approaches to monitor Twitter in real-time for the
Data collection and preparation
We use the Twitter API v1.0 for our data collection. The API v1.0 offered by the Twitter service is a moving target, and has changed several times even over the course of our investigation. The most common and consistent method for gathering data is to request a paged set of data for a given query. The subject (brand) selected for this research is Justin Bieber. At the time of this research, his Twitter account was the largest Twitter account receiving more than 300,000 tweets daily, eclipsing
Feature engineering
The Twitter sentiment analysis is a special case of the general category of text classification. Text classification problems are complex in nature and are always characterized by high dimensionality (Yang & Pedersen, 1997). To reduce this complexity researchers begin by applying preprocessing techniques to the original documents in order to produce a more simplified text.
We use standard preprocessing activities in our feature engineering stage. These are: (1) removing stop words, (2) stemming,
Automated, supervised sentiment analysis
As stated earlier, we consider Twitter sentiment analysis as a text classification problem. We use DAN2 and SVM as two methods for this analysis. These methods are supervised machine learning approaches that require a training dataset for their learning stage. Once each model is trained, they can be used to automatically provide sentiment associated with previously unseen input (tweets).
Once the feature set is defined, in order to assess a tweet’s or a corpus’s sentiment, a functional form and
Conclusion
This research makes several contributions to Twitter sentiment analysis, demonstrated through application on a corpus of tweets related to the Justin Bieber brand. Earlier research on Twitter classification has classified factual sounding tweets as a neutral tweet (Go et al., 2009). Using this approach, they state that “more than 80%” of tweets contain no sentiment. Our approach to sentiment analysis has increased sensitivity, accounting for tweets with mild sentiment (positive and negative),
References (63)
- et al.
Twitter mood predicts the stock market
Journal of Computational Science
(2011) - et al.
Measuring effectiveness of a dynamic artificial neural network algorithm for classification problems
Expert Systems with Applications
(2010) - et al.
Automated text classification using a dynamic artificial neural network model
Expert Systems with Applications
(2012) - et al.
A dynamic architecture for artificial neural network
Neurocomputing
(2005) - et al.
Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums
ACM Transactions on Information Systems
(2008) - Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. (2011). Sentiment analysis of twitter data. In...
- Aue, A., & Gamon, M. (2005). Customizing sentiment classifiers to new domains: a case study. In Proceeding of the intl....
- Bakshy, E., Hofman, J., Mason, W., & Watts, D. (2011). Everyone’s an influencer: Quantifying influence on Twitter. In...
- Barbosa, L., & Feng, J. (2010). Robust sentiment detection on twitter from biased and noisy data. In Proceedings of the...
- et al.
Streaming trend detection in Twitter
International Journal on Web Based Communities
(2013)
Yahoo! for Amazon: Sentiment extraction from small talk on the web
Management Science
Data structures and algorithms: Information retrieval
A dynamic artificial neural network model for forecasting time series events
International Journal of Forecasting
#Occupy wall street: Exploring informal learning about a social movement on Twitter
American Behavioral Scientist
Real-time Twitter sentiment toward thanksgiving and christmas holidays
Social Networking
Twitter power: Tweets as electronic word of mouth
Journal of the American Society for Information Science and Technology
Making large-scale SVM learning practical
Cited by (403)
Bayesian game model based unsupervised sentiment analysis of product reviews
2023, Expert Systems with ApplicationsScientometric review and analysis of recent approaches to stock market forecasting: Two decades survey
2023, Expert Systems with Applicationsk-anonymization of social network data using Neural Network and SVM: K-NeuroSVM
2023, Journal of Information Security and ApplicationsExploring Variations in Corporations’ Communication After a CA Versus CSR Crisis: A Semantic Network Analysis of Sustainability Reports
2024, International Journal of Business CommunicationSemantic Analysis of Amazon Customer Using LSTM
2024, AIP Conference Proceedings