@article{MAKHILLJEAS2018131116287,
    title = {Multi-Level Tweets Classification and Mining using Machine Learning Approach},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {11},
    pages = {3907-3915},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.3907.3915},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.3907.3915},
    author = {Abdul,Suresh and},
    keywords = {SVM,Sentiment analysis,KNN,isolation,machine learning,analytical},
    abstract = {Sentiment analysis comes under study within natural language processing. It helps in finding the
sentiment or opinion hidden within a text. This research focuses on finding sentiments for twitter data as it is
more challenging due to its unstructured nature, limited size, use of slangs, misspells abbreviations, etc. Most
of the researchers dealt with various machine learning approaches of sentiment analysis and compare their
results but using various machine learning approaches in combination have been underexplored in the
literature. This research has found that various machine learning approaches in a hybrid manner gives better
result as compared to using these approaches in isolation. Moreover, as the Tweets are very raw in nature, this
research makes use of various preprocessing steps, so that, we get useful data for input in machine learning
classifiers. This research basically focuses on two machine learning algorithms K-Nearest Neighbours (KNN)
and Support Vector Machines (SVM) in a hybrid manner. The analytical observation is obtained in terms of
classification accuracy and F-measure for each sentiment class and their average. The evaluation analysis
shows that the proposed hybrid approach is better both in terms of accuracy and F-measure as compared to
individual classifiers.}
    }