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Microblogging has become a daily routine for most of the people in this world. With the help of Microblogging people get opinions about several things going on, not only around the nation but also worldwide. Twitter is one such online social networking website where people can post their views regarding something. It is a huge platform having over 316 Million users registered from all over the world. It enables users to send and read short messages with over 140 characters for compatibility with SMS messaging. A good sentimental analysis of data of this huge platform can lead to achieve many new applications like – Movie reviews, Product reviews, Spam detection, Knowing consumer needs, etc. In this paper, we have devised a new algorithm with which the above needs can be achieved. Our algorithm uses three specific techniques for sentimental analysis and can be called a hybrid algorithm – (1) Hash Tag Classification for topic modeling; (2) Naïve Bayes Classifier Algorithm for polarity classification; (3) Emoticon Analysis for Neutral polar data. These techniques individually have some limitations for sentimental analysis.

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The prices in the stock market are dynamic in nature, thereby pretend as a hectic challenge to the sellers and buyers in predicting the trending stocks for the future. To ensure effective prediction of the stock market, the chronological penguin Levenberg–Marquardt-based nonlinear autoregressive network (CPLM-based NARX) is employed, and the prediction is devised on the basis of past and the recent rank of market. Initially, input data are subjected to the features extraction that is based on the technical indicators, such as WILLR, ROCR, MOM, RSI, CCI, ADX, TRIX, MACD, OBV, TSF, ATR and MFI. The technical indicator is adapted for predicting the stock market. The wrapper-enabled feature selection is employed for selecting the highly significant features that are generated using the technical indicators. The highly significant features of the data are fed to the prediction module, which is developed using the NARX model. The NARX model uses the CPLM algorithm that is formed using the integration of the chronological-based penguin search optimization algorithm and the Levenberg–Marquardt algorithm. The prediction using the proposed CPLM-based NARX shows the superior performance in terms of mean absolute percentage error and root mean square error with values of 0.96 and 0.805, respectively.