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The Role of AI In Sentiment Analysis

The Role of AI In Sentiment Analysis

Artificial intelligence is a simulated cognitive ability of a machine which is programmed in such a way that it learns from the fed data (also termed as machine learning) and makes decisions or actions based on that to successfully achieve goals or to accomplish some task. Artificial intelligence has prominently become an important part of the technology industry and has solved many challenges of computer science. Robotics Automation, human speech recognition, biometrics, text analytics and natural language processing and remote sensing.

Artificial Intelligence is mainly about enabling a machine to think and make decisions like a human by training them (machines) with certain algorithms. These algorithms are developed based on mathematical understanding. Since machines do not understand the natural language spoken by humans, software are needed to make machines understand the instructions.

NLP

There are many ways in which businesses are benefited from AI technologies. Among various applications of AI, one interesting subset area is natural language processing aka NLP.

The objective of NLP is to fill the gap between what human speaks (natural language) and what machine understands (machine language).

The process of NLP makes a machine capable of interpreting the language spoken by humans and make smart predictions. NLP market size is huge and is expected to grow even more as more and more businesses are shifting towards NLP to find insights and their customer preferences from a large amount of data available online.

Among many applications of NLP, one interesting application that we are keen to research in is a smart system that helps in making decisions for investing in cryptocurrency trade.

It will give a good idea about which currency to invest in or not. This system maps the human sentiments about the cryptocurrencies to the possible rise or low in the market.

BUILDING SENTIMENT ANALYSIS TOOL

The analysis of sentiments is done on a large data available in the form of tweets, emails, public posts, and blogs. Processing and analyzing the raw texts for emotions is a complicated task. The basic model of sentiment analysis will incorporate the following processes.

The Role of AI In Sentiment Analysis

1. Data collection – gathering data from the market survey and sources like social media, twitter, public posts, forums, discussions.

For instance, if we want to collect data from twitter we will first connect to it and then search for the relevant tweets. We will filter the data on the basis of some hashtags or keyword.

2. Connectivity – collect the data with authenticity through API’s provided by resources for eg twitter API for collecting tweets about a particular subject.

Twitter provides its own API’s to get twitter data. In order to use Twitter’s API, an authentication is required. For gaining the credentials go to the Twitter application console and create a new application by logging in first. Go to the “create the new application” tab and obtain consumer key, consumer secret, access token, access token secret. These credentials will authorize you to get access. After that, we need to connect to the twitter. Tweets can be harvested in near real-time from streaming API  through Hosebird client. Hosebird is the java based client for harvesting twitter data through Twitter API.

Below is a short code snippet :

// An appropriate sized blocking queue
BlockingQueue<String> queue = new LinkedBlockingQueue<>(10000);
//Authenticate via OAuth
Authentication auth = new OAuth1(consumerKey, consumerSecret, token, secret);
// Build a hosebird client
Client client = new ClientBuilder()	.hosts("https://stream.twitter.com")
 .endpoint(endpoint).authentication(auth).processor(new StringDelimitedProcessor(queue)).build(); client.connect();

//Establish a connection 
client.connect();

//Do the needful with the messages
for (int msgRead =0; msgRead < 100; msgRead++) {
  String msg=queue.take();
   System.out.println(msg);
}

3. Data structuring – Data first needs to be processed before analyzing which means cleansing the text data from unnecessary links, advertisements, tags as these will make the machine predictions inaccurate. For example, cleaning the data of unnecessary links, spam, and promotional texts.

4. Data upload– keep the structured data in some database or server.

5. Model building – develop an analytical program which can process and feed the data to the machine for understanding using a programming language.

6. Choose an algorithm for training the machine by testing and cross-checking – Choosing an algorithm for analyzing the text data is a tricky task. There are several algorithms out there like Naive Bayes, Decision tree, K – means, Fuzzy C-means etc. Every algo performs differently on different datasets. Their accuracy might vary. Proper research and testing should be done before choosing one. Given below is a short code demonstrating a text classifier which uses Multinomial Naive Bayes using Datumbox machine learning framework. You can also use Datumbox API for which you will have to first get the Datumbox API key. To access the Datumbox API, you need to sign up and visit your API Credentials panel where you can get your API Key.

To use the Datumbox framework in your maven project, add the following dependency to the pom.xml

<dependency>
<groupId>com.datumbox</groupId>
<artifactId>datumbox-framework-lib</artifactId>
<version>0.8.1</version>
</dependency>


//map data data set files

Map<String, URL> trainingFiles = new HashMap<>();


// Giving text file containing different polarity (positive/negative,neutral...or may be more )texts as training files

trainingFiles.put("Positive",NaiveBayesExample.class.getResource("/positive.txt"));

trainingFiles.put("Negative",NaiveBayesExample.class.getResource("/negative.txt"));


// setting MultinomialNaiveBayes class to training parameters for implementing Multinomial Naive Bayes model.

trainingParameters.setModelerTrainingParameters(new MultinomialNaiveBayes.TrainingParameters());

7. Train the model with a prepared training dataset.

8. Check the results and make corrections to increase accuracy.

9. Integrate the model in an application.

10. Generate business reports.

Sentiment analysis is a complicated task and the sentiment tool at first might not be very accurate as it learns from the data fed to it. Predictions will be wrong from time to time but it can be improved to more than 50-60 percent by training the machine with more accurate results.

This tool will help understand the public’s reaction to the news on Twitter and measure the voice of people and their opinions on cryptocurrency.

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