Automatic new topic identification using multiple linear regression pdf

Li, B. Zhang, Q. Li, R. Tao, and N. Health Inform. Ortega, H. Fabelo, R. Camacho, M. Plaza, G. Callico, and R. Express 9 2 , — Zhu, K. Su, Y. Liu, H. Yin, Z. Li, F. Huang, Z. Chen, W. Chen, G. Zhang, and Y. Express 6 4 , — Lu and M. Khouj, J. Dawson, J. Coad, and L. Lu and B. Calin, S. Parasca, D. Savastru, and D. Parasca, M. Calin, D. Manea, S. Miclos, and R. Express 9 11 , Halicek, J. Dormer, J. Little, A. Chen, and B. Express 11 3 , Jian, Z. Zhang, and W.

Express 10 12 , — Mordant, I. Al-Abboud, G. Muyo, A. Gorman, A. Sallam, P. Ritchie, A. Harvey, and A. Johnson, D. Wilson, W. Fink, M. Humayun, and G. Gao, R. Smith, and T. Express 3 1 , 48—54 Schweitzer, J.

Horn, R. Mikolajczyk, G. Krause, and J. Yong, X. Fang, Z. Qi, C. Yan, J. You, and L. Xiang, F. Nie, G. Meng, C. Pan, and C. Neural Networks and Learning Syst. Zhang, L. Xiang, and C. Wen, Y. Xu, Z. Li, Z. Ma, and Y. Fang, Y. Xu, X. Lai, W. Wong, and B. Shao, Y. Xu, L.

Liu, and J. Li, H. When to use multiple regression modelling 3 With observational data in order to produce a prognostic equation for future prediction of risk of mortality e.

Predicting future risk of CHD used year data from the Framingham cohort. When to use multiple regression modelling 4 With observational adjust for possible. Definition of Confounding A confounder is a factor which is related to both the variable of interest explanatory and the outcome, but is not an intermediary in a causal pathway.

But, also worth adjusting for factors only related to outcome Lung Cancer. In a causal pathway each factor is merely a marker of the other factors i. Error Beta 2. Lower Bound Upper Bound. Unstandardized Coeff icients B Std. Error 1. How do you select which variables to enter the model? Usually consider what hypotheses are you testing?

If main exposure variable, enter first and assess confounders one at a time For derivation of CPR you want powerful predictors Also clinically important factors e. How do you decide what variables to enter in model? With great difficulty! Approaches to model building 1. Let Scientific or Clinical factors guide selection 2. Use automatic selection algorithms 3. A mixture of above. Add BMI and smoking?

Baseline LDL 2. Adherence 4. BMI and smoking Is this a good model? Should I leave out the non-significant factors Model 2? Adj R2 lower, F has increased and number of parameters is less in 2nd model. Is this better? Kullback-Leibler Information Kullback and Leibler quantified the meaning of information related to Fishers sufficient statistics Basically we have reality f And a model g to approximate f So K-L information is I f,g. We want to minimise I f,g to obtain the best model over other models I f,g is the information lost or distance between reality and a model so need to minimise:.

Generalized linear models. Generalized linear models: Model Add all factors and covariates in the model as main effects. Difficult choices with Gender, smoking and BMI 3. AIC only changes by 1. Generally changes of 4 or more in AIC are considered important.

Conclude little to chose between models 2. AIC actually lower with larger model and consider Gender, and BMI important factors so keep larger model but have to justify 3. Model building manual, logical, transparent and under your control. Disadvantages Non stable selection stepwise considers many models that are very similar P-value on entry may be smaller once procedure is finished so exaggeration of p-value Predictions in external dataset usually worse for stepwise procedures.

Remember Occams Razor Entia non sunt multiplicanda praeter necessitatem Entities must not be multiplied beyond necessity. The study helped to design a model which can facilitate future business researches for predicting product sales in an online environment.

The main objective of the project is to show that product demands can be predicted through the comparative influence of promotional marketing strategies such as discounts and the provision of free delivery choices, user generated contents such as volume and valence of on-line reviews ,and sentiments of the web reviews.

After getting the data, the texts of reviews are then processed using natural language processing NLP algorithm. The resulted sentiment is labelled as positive, negative or neutral for further analysis. This study will then use a Multiple Linear Regression to predict product sales, as well as to predict the effects of the online sentiments on the same so as to design effective promotional strategies and sales tactics. Web scraping will be done using a web crawler.

Wrapper program would be used to detect templates in source. Required real time data is gathered and copied from the web and stored in a file for process. The paper proposes that promotional marketing strategies and social interactions such as online review and answered questions are both important for influencing sales. The paper shows that sentiments has a significant interaction with volume and valence of online review and could significantly affect and predict product sales.

In summary, we have shown that when sentiments interacts with volume and valence, it becomes a more important predictor of product sales.

East, R. Cui, G. Professor Deven Ketkar ISSN: PDF Version View. G Student, Dept. An emerging area in prediction of sales is in big data and user-generated content on the sales of product. Appropriate parameters were not considered There is certain correlation between the parameters which affect the sales. Classification Algorithm used for Classification: Natural Language processing algorithms -It is concerned with the interactions between computers and human languages. Parameter that algo uses: Customer Reviews.