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International Journal Advanced Research Engineering and Technology (IJARET) of inVolume 12, Issue 3, March 2021, pp.311-321 Article ID: IJARET_12_03_031 Available online ttp://iaeme.com/Home/issue/IJARET?Volume=12&Issue=3 at hISSN Print: 0976-6480 and ISSN Online: 0976-6499 DOI: 10.34218/IJARET.12.3.2021.031

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STOCK MARKET PREDICTION USING SENTIMENTAL ANALYSIS - A VADER

APPROACH Pavan Krishna, S Fahad Kamraan and Priyanka

Department of Computer Science and Engineering, SRM University AP-Amarava , Indiati

ABSTRACT Stock markets are appealing to retail investors or the general public because of their

highly rewarding nature. But the working dynamics of the stock market are complex and difficult to understand, so making an informed decision would not be simple and easy. Many schemes are proposed which help in making a decision based on statistical or

historical data. But there are many other factors that affect the price of the stock. Factors such as politics, company- related factors (product launches, new investments, etc), investor sentiments, interest rates, and future events In this paper, we are proposing a new method of predicting stocks by performing sentimental analysis on the financial data. A sentiment is analyzed on the subjectivity and polarity index by classifying it into positive and negative news. The data from Twitter, financial news are analyzed based on subjective and polarity indexes. The proposed scheme has achieved 91% accuracy using Linear scriminant Analysis. Di

Key words: Sentimental Analysis, Natural Language Pro- cessing, Stock Markets, Prediction.

Cite this Article: Pavan Krishna, S Fahad Kamraan and Priyanka, Stock Market Prediction Using Sentimental Analysis - Vader Approach, A International Journal of Advanced Research Engineering and Technology (IJARET)in , 12(3 2021, pp. 311-),321. ttp://iaeme.com/Home/issue/IJARET?Volume=12&Issue=3 h

1. INTRODUCTION Financial markets are quite an alluring proposition especially the stock markets. In India alone, the number of Demat accounts stands at a whopping 40.8 million as of fiscal 2020[1]. The number of investors from small towns is also on the rise[2] irrespective of the SENSEX and

NIFTY losing by 23.8% and 26.03% respectively (FY) 2020 alone. However, financial inliteracy and the knowledge of financial markets is not increasing[5] all around the world. One

who understands the signals, technical i whole ndicators, and companies’ books can bring a different perspective together which in turn has an effect on how the market plays out. News is one of the prime factors that affect stock prices date, moreover, the likes of micro-in today’s

Stock Market Prediction Using Sentimental Analysis - A Vader Approach


blogging sites like Twitter highly influence the public sentiments thus having a direct co-relation to market runs. [6]

Sentimental analysis is used to help find patterns in textual data, to understand the people’s emotions and sentiments in a much deeper and holistic way. is used to find the polarity of Itthe information and to classify it under different emotion/ sentiment radar all the way from sad to happy to excited[7]. These latest advancements in Natural Language Processing (NLP) has made analyzing the stock markets in real- much easier. time

2. RELATED WORK Till now, forecasting the stock prices has been done in various ways, among those the quite popular and high result yielding are those which forecast based on stock prices, textual news, and news sentiments. Forecasting using stock prices and previous historical data is quite a popular field of work. Many researchers using various deep learning and normalization techniques have devised a model that takes a plethora of data from the markets and emits out a prediction is also one of the approaches.[8]

Other research involving neural networks with statistical analysis which is a heuristic method and output corresponding was either Yes or No; Up or Down. Neural Networks are chosen for their ability to deal with uncertain, fuzzy, or insufficient data which fluctuate rapidly in very short periods of time but they require a very large number of previous cases also for complicated networks statistical relevance was missing. There are various methods by which the analysis has been done in the past [16] like The idea of Recurrent Neural Network (RNN) where input data were not independent of each other. By knowing the previous iterations’ data

will improve our prediction accuracy which was proved but only up to few iterations.[17] RNN was based on the polarity of statement alone since it is proposed [18] and memory to

build the neural network are identified as the limitation of RNN. To overcome the problem of memory LSTM was proposed which had gated cells to store the information for long term than RNN.[19] In order to improve accuracy of LSTM’s there are approaches like -LSTM (Target TD

Dependent LSTM), -LSTM (Attention LSTM) others. TD-LSTM is obtained by AT and incorporating target information of aspect if it is stocks, movies or songs. [20] -LSTM which ATwas adding the more specifics of aspects as a vector to LSTM.[21] So, LSTM was able to do SA at a document level but it was difficult to train them for different context of problem statement.

There was another scheme that successfully embeds the interdependencies of sentences or words into vector space called Hierarchical Neural Network. This model builds and elaborates

the inter-word and inter-sentence relations so that complex statements could also be analysed.[22] There was model which is proposed by combining HNN and LSTM, in this method Wor embeddings are fed into a sentence-level bidirectional LSTM along with concatenated d

sentence-level vectors of forward and backward LSTM outputs and finally fed into a bidirectional document-level LSTM.[23]

Though HNN were efficient to deal complex statements it was not efficient with complex sentiments. The CNN which are known to be most efficient of all the Neural Networks had a proposed scheme where a deep neural network model is combined with domain knowledge and convolutional neural network. After extensive experiment proved that a positive influence on sentiment classification was due to domain knowledge like sentiment scores.[24] The CNN was extended to multiple modes like audio, video beyond the document analysis which is named as Multimodal Sentimental analysis.[25] The CNN has models will certainly give higher accuracy of all but it consumes lot of time. Sentimental analysis was also carried out by Lexicon Based Approach with different techniques. There is method that is a semantic technique data-driven sentiment lexicon construction method that has high-coverage and high-quality.[26] Similarly

Pavan Krishna, S Fahad Kamraan and Priyanka


there is Dictionary technique for Lexicon approach that is fast to build the lexicon and itsquality pretty good.is [27]

Slowly researchers have proposed Hybrid schemes where multiple neural networks are combined together to extract sentimental analysis. There was a Aspect based Sentimental

Analysis where the aspects are - rived from news headlines using RNN and LSTM, de Multichannel CNN for sentimental Analysis.[28] The drawback was of execution and time

accuracy produced based on LDA classifier less than our model. Another model which is isextension of previous is by calculating the polarity of the statement using End-End Neural Network. This was faster than existing Hybrid but not as fast as our proposed model.[29] It was difficult to produce higher performance to different dataset for model which built using a isCNN and multiple SVM(s)

[30] A fixed network architecture (topology) that can work for all cases is unknown. The limitation still exists for stocks that do not have any previous data, e.g. newly founded

companies. On other hand, for forecasting using textual news and sentiments techniques like word embedding, word to vector conversion, and data mining are being used alongside Naive’s

Bayes approach.[9]

3. METHODOLOGY In order to predict the real-time price of the stock we need to analyse the market behaviours. Twitter the micro-blogging site boosts the market sentiments, and has larger impact over the trading wall too. Therefore, analysing people sentiments using Sentimental analysis into positive, negative polarity helps in predicating the stock price more accurately using the polarity scores and subjectivity scores. Together they will be calculated based on the rules for each lexicon. Our framework is made in such a way where we take the sentimental analysis data and pass it into different classifiers to get more informed predication with higher accuracy. Fur- ther in each classifiers Decision Tree, SVM, Random Forest, LDA (Linear Discriminant Analysis) accuracy, precision, score, recall are calculated. f1

The framework is shown in Fig 1, details of each part are explained below:

3.1 Data & Pre-processing In this paper, we have used and studied the Dow Jones Industrial Average Index (DJIA) data of 6 years as well as corresponding news and Twitter data for the same.

The two-dimension DJIA data reduced to 5 n scale is used for the stock price and is x indicated as SP i.

News from various resources is used to get the latest information such product launches, ascompany news, and updates, market sentiments, and runs, etc.

It is denoted by Ni Finally, real-time data from Twitter is used to understand people’s emotions in a comprehensive way. It is denoted by RTi.

SP = SPi — SPi = [Date, Open, High, Low, Close, Volume, Adj Close] N = Ni —Ni = [Date, Source, Headline, Text] RT = RTi — RTi = [Date, HashBool, Text, Hash]

All of this data is further pre-processed using existing popular techniques like Data Cleaning, Data Reduction, Data Integration, and Transformation as shown in Fig 1.



Figure 1 Proposed Framework

3.2 Sentiment Analyzer Ni and RTi data goes through a sentiment analyzer which generates the subjectivity and polarity score by classifying into 4 different classes namely:

• Negative • Neutral • Positive and • Compound

Valence Aware Dictionary and sentiment Reasoner (VADER) is used for the classification mentioned above. The polarity score is used to find the emotion of the text in News and Twitter

while the subjectivity score is used to understand whether the news/ data is subjective or opinionated to the author or a factual statement. WPi, WSi weights gets attached to the main

data frame post analyzing. This main data frame D i is thus used for the different models mentioned below to predict the price of the stock.



3.3 Decision Tree Decision Tree is a popular supervised machine learning model, which is influenced by a tree-like structure. It has 3 components namely, Node, Edge, and Leaf. A decision tree creates to itall the possible decisions and defines something like a set of rules which an inference can fall into and does regression or classification according to that. The sample instance starts from the top of the tree known as the root and it sorts down all the way down a single leaf. to

Figure 2 Decision Tree

3.4 Support Machine (SVM) Vector Support Vector Machine is also a supervised machine learning model like a decision tree which is used for both classification and regression. It is quite a popular model too and is known for gaining a better accuracy with a low computation power. Support Vector Machine is best suited for extreme cases as in cases that have extreme data points (also known as support vectors). The objective of the algorithm is to find a support vector with a maximum margin or that has a more area of the hyperplane. [10]

Figure 3 Support vector representation

As seen in Fig-3 the distance of the support vector to the positive side is denoted by D+ and that to negative by D-. Fig-3 is a representation of the Linear Support Vector Machine (LSVM) which is used for linear data. Various techniques like K-Fold, Gradient are used for the Non-linear data [11] since it needs to get converted into 3D to differentiate the classes.



3.5 Random Forest Random Forest is a collection of decision trees that act together. Each individual tree spits out a prediction and the average of all those predictions is taken as the output. Random Forest is also can be used for both classification and the regression task. It maintains the accuracy for the

missing data and overfit the model. It can also handle larger data sets with higher won’tdimensionality since growing more trees making it more efficient. But random forests are not widely used for statistical modeling because of their black-box approach. [12]

3.6 Linear Discriminant Analysis (LDA) The idea behind Linear Discriminant Analysis to reduce the number of dimensions to single-isaxis/

line thus the name linear. [13] Linear Discriminant Analysis tries to minimize the variance and maximize the distance between classes[14]. It calculates the global mean (M) from both the classes and computes the Mean vector as well as the covariance matrix for the matrix latter to compute a scatter matrix to create discriminant functions.

Discrmininat Functions are created using the formula below: Fi=Mi *C-1* X - 0.5*Mi *C-1*MiT+ln(Pi) T

Where: • Fi the Discriminant function of class i is • Mi the mean vector of class i is • XT is the new data and • Pi the probabili of the prior class i is ty

When (WSi*WPi*Ni) data is passed through the models, the output should be equal to SPiand once the model is trained (WSi*WPi*Ni) is replaced with RTifor real-time prediction.

Algorithm: The flow of the methodology in Fig. 4 is discussed below. Step 1: Import the libraries which are Pandas, Matplotlib, Re for regular expressions used

for Data Processing, ntlk, Vader sentiment package for sentimental analysis and datasets which contains Headlines of Newspaper [14], stock prices of firm. [15]

Step 2: It goes through Data Cleaning where we are inserting all the headlines into a list. This list is cleaned or processed also using regular expressions to remove next line, quotes in the list. This list is now converted into a string which is a collection of sentences or paragraph. The sentimental analyser takes the input as the paragraph to get the desired result.

Step 3: This the complete phase of Sentimental Analysis. Among various approaches we have chosen VADER (Valence Aware ctionary and sentiment Reasoner) which a lexicon Di isand rule-based sentiment analysis tool. This sentiment analysis algorithm is used to identify the opinion/sentiment that each headline may hold towards a financial company using polarity and subjectivity indexes. The polarity index is where every statement quantified with a positive isor negative value. From the polarity sign inferred the statement positive, negative and it’s if isneutral. The subjectivity index is to depict how relatable the sentence to the company. is

Step 4: The data is further divided into training data and testing data and derive feature and target dataset.

Step 5: Checking the accuracy of the model using various classifiers for different parameters like pre- cession, recall which means how many predictions are true in the factual data also

known as true positives and F1 Score which is calculated as 2*((precision*recall)/(precision+recall)).



Figure 4 Flowchart of proposed method

4. RESULTS In this section we will discuss the output of the method implemented. Label as shown in Fig 5, which is calculated by the Sentiment Intensity Analyser spits out a categorical data in 0 and 1.

Categorical data in- turn helps classifiers achieve good accuracy. The details about the confusion matrix, precision, recall, F1 Score, Accuracy and other parameters that are calculated are represented in this section.

Figure 5 Sentimental Analysis output

When the data is fed to the four models namely, cision Tree, Support Vector Machine, DeRandom Forest, and Linear Discriminant Analysis generated reports as shown in the below tables and the formulae for each of the term mentioned below. is as



Figure 6 Decision Tree Classification Report

Linear Discrimination Analysis achieved the highest accuracy because of it’s dimensionality reduction and the discriminant function used. It showed the highest accuracy and is followed by Random Forest then SVM and Decision Tree Classifiers as mentioned below in the Table.

Table 1 Final Results

Classifier Precision Recall F1 Score Accuracy Decision Tree 60% 60% 60% 60% SVM 58% 60% 45% 60% Random Forest 76% 68% 61% 68% LDA 91% 91% 91% 91%

Figure 7 SVM Classifier Confusion Matrix Classification Report

Figure 8 Random Forest Classifier Confusion Matrix Classification Report



Figure 9 LDA Classifier Confusion Matrix Classification Report

5. CONCLUSION Making an informed decision in stock markets is highly difficult for retail investors, although they are highly rewarding and tend to attract a lot of investors they are hard to grasp because oftheir complex working dynamics. Moreover the existing historical and statistical analysis does not represent the future of the stock accurately because of several factors and events which

influence the price. In this paper, we use sentimental analysis to depict sentiments and to achieve results with 91% accuracy using subjectivity and polarity index coupled with linear discriminant analysis. Twitter data along with financial news data is analyzed based on subjectivity and objectivity score as well as a polarity score is generated by categorizing it into positive and negative news to find trends and predict the price of the stock in real-time.

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