Use Sentiment Analysis With Python to Classify Movie Reviews

In this tutorial, you’ll learn about sentiment analysis and how it works in Python. You’ll then build your own sentiment analysis classifier with spaCy that can predict whether a movie review is positive or negative.

Sentiment analysis is a powerful tool that allows computers to understand the underlying subjective tone of a piece of writing. This is something that humans have difficulty with, and as you might imagine, it isn’t always so easy for computers, either. But with the right tools and Python, you can use sentiment analysis to better understand the sentiment of a piece of writing.

Why would you want to do that? There are a lot of uses for sentiment analysis, such as understanding how stock traders feel about a particular company by using social media data or aggregating reviews, which you’ll get to do by the end of this tutorial.

In this tutorial, you’ll learn:

• How to use natural language processing (NLP) techniques
• How to use machine learning to determine the sentiment of text
• How to use spaCy to build an NLP pipeline that feeds into a sentiment analysis classifier

This tutorial is ideal for beginning machine learning practitioners who want a project-focused guide to building sentiment analysis pipelines with spaCy.

You should be familiar with basic machine learning techniques like binary classification as well as the concepts behind them, such as training loops, data batches, and weights and biases. If you’re unfamiliar with machine learning, then you can kickstart your journey by learning about logistic regression.

Using Natural Language Processing to Preprocess and Clean Text Data

Any sentiment analysis workflow begins with loading data. But what do you do once the data’s been loaded? You need to process it through a natural language processing pipeline before you can do anything interesting with it.

The necessary steps include (but aren’t limited to) the following:

1. Tokenizing sentences to break text down into sentences, words, or other units
2. Removing stop words like “if,” “but,” “or,” and so on
3. Normalizing words by condensing all forms of a word into a single form
4. Vectorizing text by turning the text into a numerical representation for consumption by your classifier

All these steps serve to reduce the noise inherent in any human-readable text and improve the accuracy of your classifier’s results. There are lots of great tools to help with this, such as the Natural Language Toolkit, TextBlob, and spaCy. For this tutorial, you’ll use spaCy.

Note: spaCy is a very powerful tool with many features. For a deep dive into many of these features, check out Natural Language Processing With spaCy.

Before you go further, make sure you have spaCy and its English model installed:

$ pip install spacy
$ python -m spacy download en_core_web_sm

The first command installs spaCy, and the second uses spaCy to download its English language model. spaCy supports a number of different languages, which are listed on the spaCy website.

Next, you’ll learn how to use spaCy to help with the preprocessing steps you learned about earlier, starting with tokenization.

Tokenizing

Tokenization is the process of breaking down chunks of text into smaller pieces. spaCy comes with a default processing pipeline that begins with tokenization, making this process a snap. In spaCy, you can do either sentence tokenization or word tokenization:

• Word tokenization breaks text down into individual words.
• Sentence tokenization breaks text down into individual sentences.

In this tutorial, you’ll use word tokenization to separate the text into individual words. First, you’ll load the text into spaCy, which does the work of tokenization for you:

>>> import spacy
>>> text = """
Dave watched as the forest burned up on the hill,
only a few miles from his house. The car had
been hastily packed and Marta was inside trying to round
up the last of the pets. "Where could she be?" he wondered
as he continued to wait for Marta to appear with the pets.
"""
>>> nlp = spacy.load("en_core_web_sm")
>>> doc = nlp(text)
>>> token_list = [token for token in doc]
>>> token_list
[
, Dave, watched, as, the, forest, burned, up, on, the, hill, ,,
, only, a, few, miles, from, his, house, ., The, car, had,
, been, hastily, packed, and, Marta, was, inside, trying, to, round,
, up, the, last, of, the, pets, ., ", Where, could, she, be, ?, ", he, wondered,
, as, he, continued, to, wait, for, Marta, to, appear, with, the, pets, .,
]

In this code, you set up some example text to tokenize, load spaCy’s English model, and then tokenize the text by passing it into the nlp constructor. This model includes a default processing pipeline that you can customize, as you’ll see later in the project section.

After that, you generate a list of tokens and print it. As you may have noticed, “word tokenization” is a slightly misleading term, as captured tokens include punctuation and other nonword strings.

Tokens are an important container type in spaCy and have a very rich set of features. In the next section, you’ll learn how to use one of those features to filter out stop words.

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Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

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.

#machine learning tutorials #machine learning project #machine learning sentiment analysis #python sentiment analysis #sentiment analysis

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

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Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development