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Sentiment Analysis Using Machine Learning

Introduction

Sentiment analysis is a machine learning tool specifically designed for polarity in texts. This has been made possible by training machine learning tools to detect sentiment in the text without any human involvement. These machine tools can sense negative polarity, positive polarity or neutral polarity. Sentiment machine learning models can be empowered to do more than just understanding mere definitions, but also to read the context, spelling mistakes and sarcasm.

How Does Sentiment Analysis Work in Machine Learning?

Several techniques can be used to train and command machine learning agents to run sentiment analysis. When used together they provide impressive results:

Naive Bayes

Naive Bayes is a probabilistic algorithm that classifies sentiment analysis. They can tell the probability of a given subject and consider it a positive or a negative.

The Bayes works in the sense that; the probability of A, B being true, is the same as the probability of B, if A is true. Multiply by the probability of A being true, and then divide by the probability of B being true.

To avoid this confusing math, Naive Bayes calculates the words against each other. In machine learning word polarity is the training conducted. Here, the probability of words is calculated to check whether a phrase, word or text is positive or negative.

To have a more accurate Naive Bayes prediction, tools like TF-IDF, stop removal and lemmatization are implemented.

Linear Regression

Linear regression is the algorithms used to get the prediction of Y-value, in this case, given X features. Machine learning examines data to identify a relationship. To make further predictions, the relationships are put along the X/Y axis. Linear regression calculates the relationship between the words and phrases(X input) and polarity(Y output).

Support Vector Machines (SVM)

Support Vector Machines is a model with similarities to linear regression but more sophisticated. It uses algorithms to classify and train text inside the sentiment polarity model. This is many steps beyond X/Y prediction. Support Vector Machines uses hyperplane to separate tags. This is like linear regression anything on the X side and anything on the Y side, which translates to positive and negative results. However, the more data becomes intensive there is the likelihood to miss out on the drawing of the line to show the classification of the two categories or camps.

When using Support Vector Machines, the more the complexity of the data the more accuracy of the predictor is achieved. Now picture a three-dimension where the Z-axis is introduced. Simply put, Support Vector Machines’ multi-dimensional nature assures more accuracy in machine learning. 

Deep Learning

Deep learning aims to mimic the human brain by calculating data using artificial neural networks. This subfield of machine learning is hierarchical, meaning it is multi-level and automatically chains several processes created by humans together. It uses multiple algorithms progressively in a step by step manner to solve complex problems as humans do.

Emotion Analysis Using Deep Learning

An emotion is a way human beings show feeling about certain things or situations; they can be anger, love, fear, joy, surprise and anger. Emotions can be seen in facial expressions, actions, speech and writing. Emotional analysis in machine learning focuses on two main expressions; speech and written text.

Emotional analysis is used in by various industries in different ways including; emotional marketing in a business where customer’s emotions are stimulated to buy certain products and services and in security firms where security agencies track text writing in messages, emails and blog posts to stay alert about suspicious activities.

Text Analysis Techniques

To understand the meaning of a word in a text and to make accurate predictions, several Natural Language Processing (NLPs) are performed. Tasks like; Parts of speech, Numeric quantities, Dates, Times, People, Structure of the sentences, Frequency of words, Nouns pointing to entities. To derive annotations in linguistics, standard CoreNLP APIs is used. Combined with algorithms, emotions can be identified in binary inputs.

Detecting emotion from text 

Deep learning facilitates formulas that understand the structure and semantics of sentences. In some instances, a deep learning algorithm could represent a whole sentence. The representation is based on interdependencies and words’ arrangements. Recurrent neural networks represent an operator as a matrix and represent the words as vectors.

The process changes the polarity and assures the sentences are semantically correct. The only disadvantage with such a system is that intensive training is a prerequisite. To solve the short memory challenges in Recurrent Neural Networks, Long Term Short Memory (LSTM) technique is used.

Social Media Sentiment Analysis Using Machine Learning

Getting information today is just a click away, social media has vast sources of information, and it keeps growing every second due to the written text being shared continuously. With this data being shared these texts can be both negative and positive opinions about different topics. Sometimes the message contains praise, agreeing or bullying.

There are a couple of tools to monitor and analyze online content, which in this case is the language and tone used to mention a certain product or service. The most popular use of sentiment analysis is of classification of a text to class. Sentiment can be classified by binary to be either positive or negative or multiclass where there are more than three classes of problems.

So how do social media platforms detect sentiment in machine learning?

Twitter sentiment analysis using machine learning

Here is how to conduct Twitter sentiment analysis:

1. Gather Twitter Data

Twitter data will help you train and test your sentiment model on Twitter data. There are many ways of extracting data from Twitter. You can use Zapier which enables different teams to connect different apps used in an organization, monkeylearn automatically conduct sentiment analysis on incoming data, IFTTT which helps connect apps for actions when certain criteria are to be met, ExportTweet, TweetDownload and Twitter API which lets you access Twitter public data.

2. Prepare Your Data

Once you have the tweets ready for the sentiment analysis, now you prepare the data. You need accurate results so cleaning the data is crucial for training purposes since the data is unstructured.

3. Create a Twitter Sentiment Analysis Model

Monkeylearn is a good tool to help you build sentiment analysis. It also provides you with various types of statistics to check the performance of your sentiment analysis classifier.

Here are steps to guide you to perform a sentimental analysis:

  1. Choose a model type
  2. Decide the classification type you’d like to do
  3. Import your Twitter data
  4. Tag data to train your classifier
  5. Test your classifier

4. Analyze Your Twitter Data for Sentiment

Once you have the sentiment analysis, integrate it with the Twitter data that needs to be analyzed. Using monkeylearn, you can do it in these three ways: Batch Analysis, Integrations and MonkeyLearn’s API.

5. Visualize Your Results

Data visualization tools aid in interpreting sentiment analysis in the simplest way possible. Monkeyleraan studio is an all under one roof data visualization and text analysis suite; it will also visualize Twitter data on feature-based sentiment analysis.

Stock market sentiment analysis using python & machine learning 

Sentiment analysis in finance

Sentiment analysis is important to finance, finance sentiment analysis is a key place for extracting insights from social media, news, financial reports, risk management, trading, and operations in banking institutions any finance-related source.

Here, the basis of argument focuses on trading and investments.

  • Uses of sentiment analysis in trading
  • Scrutinize news articles or tweet in a swift and fire a trade forthwith.
  • Peruse robust output insights and financial reports 
  • Collect insights from the public by analyzing web forums, social media, analysts’ reports and news

Using Python to predict stock prices with Seeking Alpha’s article headlines 

These steps are to be followed using python:

Collate article dates and headlines 

Here are the steps for collating headlines:

  • Visit SeekingAlpha.com, search for the entity you want 
  • Copy and paste to Excel spreadsheet
  • Delete duplicates and unwanted data

Process the text by importing and cleaning

Here are the steps to arrive at a clean data:

  • Decide the end goal
  • Sort out the dates
  • Transform the cleaned date to a DateTime format
  • Clean and convert data frame

Perform sentiment analysis and generate a score index

Churning is an important process. Here headlines are sent to the sentiment analysis to be churned out. The best model for this is the one trained by supervised machine learning; the trained data, in this case, is the historical financial data. The trained model can be used for future headlines to evaluate the sentient.

Correlate lagging indicator against prices

Compare the relationship between the company stock returns and your sentiment score. The sentiment score will have a predictive value if there is a significant relationship.

Here are the steps to follow:

  • Aggregate daily sentiment scores
  • Import company’s prices and calculate returns
  • Check the relationship between returns against lagged score daily

Final Thoughts

By and large, you need to have a deep understanding of the reason behind your trade. Come up with speculation and test it fittingly. Separate the variables you will be testing and then divide your data into in and out-of-sample pieces.

Trading can be fun and stressful at the same time, it is a clever way to make money. You need to outsmart others by learning strategies, you also need to be creative and stay updated to stay afloat. So trade carefully.

Topics in Machine Learning

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