Arne  Denesik

Arne Denesik

1603479600

Hands-On Guide To Using AutoNLP For Automating Sentiment Analysis

Automated Machine learning or autoML is used for automating the complete process of machine learning for real-world problems to make the process easier and more efficient. Over the years researchers have developed ways of automating processes by developing tools like AutoKeras, AutoSklearn and even no-coding platforms like WEKA and H2o.

One such area of automation is in the field of natural language processing. With the development of AutoNLP, it is now super easy to build a model like sentiment analysis with very few basic lines of code and get a good output. With automation like these, it allows everyone to be a part of the machine learning community and does not restrict machine learning to only developers and engineers.

In this article, we will learn about what AutoNLP is and implement a sentiment analysis model with twitter dataset.

What is AutoNLP?

Using the concepts of AutoML, AutoNLP helps in automating the process of exploratory data analysis like stemmingtokenization, lemmatization etc. It also helps in text processing and picking the best model for the given dataset. AutoNLP was developed under AutoVIML which stands for Automatic Variant Interpretable ML. Some of the features of AutoNLP are:

  1. Data cleansing: The entire dataset can be sent to the model without performing any process like vectorization. It even fills the missing data and cleans the data automatically.
  2. **Uses feature tools library for feature extraction: **Feature Tools is another great library that helps in feature engineering and extraction in any easy way.
  3. **Model performance and graphs are produced automatically: **Just by setting the verbose, the model graph and performance can be shown.
  4. **Feature reduction is automatic: **With huge datasets, it becomes tough to select the best features and perform EDA. But this is taken care of by AutoNLP.

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Hands-On Guide To Using AutoNLP For Automating Sentiment Analysis
Ian  Robinson

Ian Robinson

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Sofia  Maggio

Sofia Maggio

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Sentiment Analysis in Python using Machine Learning

Sentiment analysis or opinion mining is a simple task of understanding the emotions of the writer of a particular text. What was the intent of the writer when writing a certain thing?

We use various natural language processing (NLP) and text analysis tools to figure out what could be subjective information. We need to identify, extract and quantify such details from the text for easier classification and working with the data.

But why do we need sentiment analysis?

Sentiment analysis serves as a fundamental aspect of dealing with customers on online portals and websites for the companies. They do this all the time to classify a comment as a query, complaint, suggestion, opinion, or just love for a product. This way they can easily sort through the comments or questions and prioritize what they need to handle first and even order them in a way that looks better. Companies sometimes even try to delete content that has a negative sentiment attached to it.

It is an easy way to understand and analyze public reception and perception of different ideas and concepts, or a newly launched product, maybe an event or a government policy.

Emotion understanding and sentiment analysis play a huge role in collaborative filtering based recommendation systems. Grouping together people who have similar reactions to a certain product and showing them related products. Like recommending movies to people by grouping them with others that have similar perceptions for a certain show or movie.

Lastly, they are also used for spam filtering and removing unwanted content.

How does sentiment analysis work?

NLP or natural language processing is the basic concept on which sentiment analysis is built upon. Natural language processing is a superclass of sentiment analysis that deals with understanding all kinds of things from a piece of text.

NLP is the branch of AI dealing with texts, giving machines the ability to understand and derive from the text. For tasks such as virtual assistant, query solving, creating and maintaining human-like conversations, summarizing texts, spam detection, sentiment analysis, etc. it includes everything from counting the number of words to a machine writing a story, indistinguishable from human texts.

Sentiment analysis can be classified into various categories based on various criteria. Depending upon the scope it can be classified into document-level sentiment analysis, sentence level sentiment analysis, and sub sentence level or phrase level sentiment analysis.

Also, a very common classification is based on what needs to be done with the data or the reason for sentiment analysis. Examples of which are

  • Simple classification of text into positive, negative or neutral. It may also advance into fine grained answers like very positive or moderately positive.
  • Aspect-based sentiment analysis- where we figure out the sentiment along with a specific aspect it is related to. Like identifying sentiments regarding various aspects or parts of a car in user reviews, identifying what feature or aspect was appreciated or disliked.
  • The sentiment along with an action associated with it. Like mails written to customer support. Understanding if it is a query or complaint or suggestion etc

Based on what needs to be done and what kind of data we need to work with there are two major methods of tackling this problem.

  • Matching rules based sentiment analysis: There is a predefined list of words for each type of sentiment needed and then the text or document is matched with the lists. The algorithm then determines which type of words or which sentiment is more prevalent in it.
  • This type of rule based sentiment analysis is easy to implement, but lacks flexibility and does not account for context.
  • Automatic sentiment analysis: They are mostly based on supervised machine learning algorithms and are actually very useful in understanding complicated texts. Algorithms in this category include support vector machine, linear regression, rnn, and its types. This is what we are gonna explore and learn more about.

In this machine learning project, we will use recurrent neural network for sentiment analysis in python.

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Origin Scale

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A Complete Guide To Sentiment Analysis And Its Applications

Introduction

Sentiment analysis is a technique through which you can analyze a piece of text to determine the sentiment behind it. It combines machine learning and natural language processing (NLP) to achieve this. Using basic Sentiment analysis, a program can understand if the sentiment behind a piece of text is positive, negative, or neutral.

It is a powerful technique in Artificial intelligence that has important business applications. For example, you can use Sentiment analysis to analyze customer feedback. You can collect customer feedback through various mediums twitter, Facebook, etc. and run sentiment analysis algorithms on them to understand your customer ‘s attitude towards your product.

How Sentiment Analysis Works

The simplest implementation of sentiment analysis is using a scored word list. For example, AFINN is a list of words scored with numbers between minus five and plus five. You can split a piece of text into individual words and compare them with the word list to come up with the final sentiment score.

eg. I love cats, but I am allergic to them.

In the AFINN word list, you can find two words, “love” and “cats” with their respective scores of +2 and -3. You can ignore the rest of the words (again, this is very basic sentiment analysis). Combining these two, you get a total score of +1. So you can classify this sentence as mildly positive.

There are complex implementations of Sentiment analysis used in the industry today. Those algorithms can provide you with accurate scores for long pieces of text. Besides that, we have reinforcement learning models that keep getting better over time.

For complex models, you can use a combination of NLP and machine learning algorithms. There are three major types of algorithms used in sentiment analysis. Let’s take a look at them.

Automated Systems

Automatic approaches to sentiment analysis rely on machine learning models like clustering. Long pieces of text are fed into the classifier, and it returns the results as negative, neutral, or positive. Automatic systems are composed of two basic processes:

Rule-based Systems

Unlike automated models, rule-based approaches are dependent on custom rules to classify data. Popular techniques include tokenization, parsing, stemming, and a few others. You can consider the example we look at earlier as a rule-based approach.

A good thing about rule-based systems is the ability to customize. These algorithms can be tailor-made based on context by developing smarter rules. However, you will have to regularly maintain these types of rule-based models to ensure consistent and improved results.

Hybrid Systems

Hybrid techniques are the most modern, efficient, and widely-used approach for sentiment analysis. Well-designed hybrid systems can provide the benefits of both automatic and rule-based systems.

Hybrid models enjoy the power of machine learning along with the flexibility of customization. An example of a hybrid model would be a self-updating wordlist based on Word2Vec. You can track these wordlists and update them based on your business needs.

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