|
| Paper |
Method |
Dataset |
Key Findings |
Limitations &Future Work |
|
|
| Kanakaraj and Guddeti (2015) |
NLP techniques, Word Sense Disambiguation, Ensemble classification |
Twitter posts on news events |
Ensemble classification improves accuracy by 3-5% over traditional ML classifiers |
Future work could explore deep learning models for further accuracy enhancement |
|
| Chirawichitchai (2014) |
Term weighting, SVM, Information Gain feature selection |
Thai text dataset |
Boolean weighting with SVM achieves the highest accuracy (77.86%) |
Future work can focus on expanding emotion classification for multilingual settings |
|
| Hogenboom et al. (2014) |
Spreading sentiment lexicon and cross-linguistic sentiment mapping |
English and Dutch language datasets |
Sentiment propagation improves accuracy by up to 47% |
Further research can investigate additional languages and domain-specific sentiment lexicons |
|
| Anjaria and Guddeti (2014) |
Supervised ML (SVM, Naïve Bayes, ANN), Unigram & Bigram features, Influence Factor |
Twitter statistics (Karnataka State Assembly Elections 2013, US Presidential Elections 2012) |
SVM achieved highest accuracy (88% for US Elections, 68% for Indian Elections) |
Future work can incorporate deep learning models and social influence factors for better prediction |
|
| Volkova, Wilson, and Yarowsky (2013) |
Understanding how gender differs in the classification of sentiment, polarity, and subjectivity |
English, Spanish, and Russian Twitter data |
Gender-based language differences improve polarity classification (2.5-5% improvement in F-measure) |
Future studies can explore additional cultural and linguistic variations for sentiment analysis |
|
