## 5 Conclusions  
In this paper a novel feature weighting scheme has been presented for the sentiment analysis of tweet data. It comprehensively measures the significance of the features in both inter-category and intra-category distribution, and an analytical model characterizing the discriminability of the feature was introduced to precisely measure how much discriminative information a feature possesses. Also, a modified Chi-square statistics model was proposed to measure the relative relevance of a feature with the corresponding category. A new fine-grained clustering strategy was adopted to properly group the features of similar inter-category distribution, which allows accurate text classification. Extensive experiments were conducted using benchmark workloads, which demonstrate that the proposed scheme is consistently superior to the existing approaches in terms of four metrics including accuracy.  
Since the proposed scheme is based on the dependence of each feature to the corresponding category, the length of the sentence does not directly affect the performance of the proposed method. A more precise model on the relation between the measures will be investigated in the future. As each word has a different feature weight reflecting its importance degree for different categories, the polarity of a sentence having two different opinions is determined by the weights obtained using the MNB text classifier. A new approach including the NN technique will also be developed for handling different or opposite opinions. In the proposed scheme the emoticons are detected and removed using the MATLAB function of the tokenized document. Since emotions usually contain some sentiments of people, a methodology effectively dealing with them will be included in future work.  
The proposed scheme is currently performed in the MATLAB environment, manually evaluating the labeled tweets. Its performance will be tested by processing real-time information of the Twitter platform via the Twitter API, and more advanced text preprocessing techniques will be incorporated to improve the quality of classification of the sentiment. Further enhancements include the portability  
Figure 6: The comparisons of the schemes with randomly formed dataset. (**a**) Accuracy; (**b**) precision; (**c**) recall; (**d**) F1-score.  
to other platforms such as mobile devices and the incorporation of Latent Dirichlet Allocation (LDA) technique to automatically identify the number of attributes of corpus [57; 58; 59; 60].  
Conceptualization, Y.W. and H.Y.Y.; Methodology, Y.W.; Software, Y.W.; Validation, Y.W. and H.Y.Y.; Formal Analysis, Y.W.; Investigation, Y.W. and H.Y.Y.; Resources, Y.W.; Data Curation, Y.W.; Writing--Original Draft Preparation, Y.W.; Writing--Review & Editing, Y.W. and H.Y.Y.; Visualization, Y.W.; Supervision, H.Y.Y.; Project Administration, H.Y.Y.; Funding Acquisition, H.Y.Y.  
This research was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2016-0-00133, Research on Edge computing via collective intelligence of hyperconnection IoT nodes), Korea, under the National Program for Excellence in SW supervised by the IITP(Institute for Information & communications Technology Promotion)(2015-0-00914), Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2016R1A6A3A11931385, Research of key technologies based on software defined wireless sensor network for realtime public safety service, 2017R1A2B2009095, Research on SDN-based WSN Supporting Real-time Stream Data Processing and Multiconnectivity), the second Brain Korea 21 PLUS project.  
The authors are grateful to the supports from the Sungkyunkwan University for sample collection.  
The authors declare no conflict of interests.