The week's pick
Random Articles
Reseach Article
Twitter Sentiment Analysis using Machine Learning and Optimization Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 179 - Number 19 |
| Year of Publication: 2018 |
| Authors: Prachi Bansal, Ramanjot Kaur |
10.5120/ijca2018916321
|
Prachi Bansal, Ramanjot Kaur . Twitter Sentiment Analysis using Machine Learning and Optimization Techniques. International Journal of Computer Applications. 179, 19 ( Feb 2018), 5-8. DOI=10.5120/ijca2018916321
Abstract
Sentiment Analysis means determining the views of the user from the text regarding that topic i.e. how one feels about it. It can be used to classify the text content into positive or negative. Various researchers have used a wide range of methods to train the classifiers for the Twitter dataset with varying results. This paper introduces a hybrid approach of using Swarm Intelligence optimization algorithms with classifiers. For each tweet, pre-processing will be done by performing various processes i.e. tokenization; removal of stop-words and emoticons; stemming. Then their feature vectors are being made by the calculation of TF-IDF and optimized with Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) before performing the binary text categorization. Naïve Bayes and Support Vector Machine (SVM) is the machine learning techniques used for the binary classification of tweets. The results drawn using optimization with classifiers is much efficient than using classifier alone.
References
- Neethu, M. S., and R. Rajasree. "Sentiment analysis in twitter using machine learning techniques." Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on. IEEE, 2013.
- Poria, Soujanya, et al. "Sentic patterns: Dependency-based rules for concept-level sentiment analysis." Knowledge-Based Systems 69 (2014): 45-63.
- Ortigosa, Alvaro, José M. Martín, and Rosa M. Carro. "Sentiment analysis in Facebook and its application to e-learning." Computers in Human Behaviour 31 (2014): 527-541.
- Saif, Hassan, et al. "Contextual semantics for sentiment analysis of Twitter." Information Processing & Management52.1 (2016): 5-19.
- Guzman, Emitza, and Walid Maalej. "How do users like this feature? a fine grained sentiment analysis of app reviews." Requirements Engineering Conference (RE), 2014 IEEE 22nd International. IEEE, 2014.
- Kontopoulos, Efstratios, et al. "Ontology-based sentiment analysis of twitter posts." Expert systems with applications40.10 (2013): 4065-4074.
- Yang, Tao, et al. "Tb-CNN: joint tree-bank information for sentiment analysis using CNN." Control Conference (CCC), 2016 35th Chinese. IEEE, 2016.
- Wehrmann, Joonatas, et al. "A character-based convolutional neural network for language-agnostic Twitter sentiment analysis." Neural Networks (IJCNN), 2017 International Joint Conference on. IEEE, 2017.
- Stojanovski, Dario, et al. "Twitter sentiment analysis using deep convolutional neural network." International Conference on Hybrid Artificial Intelligence Systems. Springer, Cham, 2015.
- Chikersal, Prerna, et al. "Modelling public sentiment in Twitter: using linguistic patterns to enhance supervised learning." International Conference on Intelligent Text Processing and Computational Linguistics. Springer, Cham, 2015.
Index Terms
Keywords