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What is NLP and why it is used

 

NLP  is a term of artificial intelligence and machine learning because by NLP  human sound or text which written on text form is change into machine-readable or understandable so that it can be used to perform a specific task using a machine not human by this get fast and effective results in the minimum time period.  NLP is set of a large number of libraries which work after installing and find the result as per your choice like if you want to find the name of male or female then it provides names corpus so you can easily find the result with help of it. Sometimes when large numbers of data is given then it is not doable by a human then we use NLP to find the best result with short times.

  • There are some reasons so that we can use it:

  • Identify Gender as per Given Corpus

  • Identify unusual words of the text file

  • Part of speech and noun identification

  • Navie buyer classification to find the word by selection part of a word

  • Tokenizing text 

  • Eliminating punctuation from tokenizing text

What do we include in NLP Assignment Help

  • Text-analysis using NLTK library

  • N-Grams 

  • Detecting text language unigrams and bigrams

  • Language identifier

  • Stemming and Lemmatization using Bigrams

  • Finding unusual words

  • part of speech and meaning

  • Name-Gender identifier

  • Classify document into categories

  • Sentiment Analysis

  • Sentiment Analysis with NLTK

  • Work with Twitter streaming and Cleaning

  • Language detection

NLP Projects

  • Sentiment analysis

  • Social media monitoring.

  • Chatbots

  • Customer Service

  • Survey Analysis

  • Managing the advertising Funnel / Targeted Advertising

  • Machine Translation 

  • Auto-correction

  • Semantic analysis

  • Text classification

  • Question Answering

  • Speech Recognition

  • Spam detection/Email Filtering

Sentiment Analysis Project Help

Sentiment Analysis is the automated process of identifying and extracting the subjective information that underlies a text. This can be either an opinion, a judgment, or a feeling about a particular topic or subject. The most common type of sentiment analysis is called ‘polarity detection’ and involves classifying a statement as

  • ‘Positive’

  • ‘Negative’

  • ‘neutral’

 

For example, let’s take this sentence: “I don’t find the app useful: it’s really slow and constantly crashing”. A sentiment analysis model would automatically tag this as Negative. And similar would be the case for happy and numb emotions.

A very common use case of sentiment analysis is twitter sentiment analysis.Sentiment analysis tools use Machine Learning and natural language processing (NLP) to organize unstructured text data automatically. Sentiment analysis algorithms are able to learn from data samples to detect the polarity of Tweets in real-time. All you have to do is train sentiment analysis tools to recognize sentiment in tweets, and they’ll do the rest. Main advantages of Twitter sentiment analysis include:

These are some of the main advantages of Twitter sentiment analysis:

Real-Time Analysis: 

Sentiment analysis is essential for monitoring sudden shifts in customer moods, detecting if complaints are on the rise, and for taking action before problems escalate. With sentiment analysis, you can monitor brand mentions on Twitter in real-time and gain valuable insights that tell you if you need to make updates.

Scalability:

Let’s say you need to analyze hundreds of tweets mentioning your brand. While you could do that manually, it would take hours of manual processing, and as your data grows it would be impossible to scale. By performing sentiment analysis you can automate manual tasks and gain valuable insights in a very short time.

Consistent Criteria: 

Analyzing sentiment in a text is subjective. when done manually. The same tweet may be viewed differently by two members of the same team. By training a machine learning model to perform sentiment analysis on Twitter data, you can use one set of criteria to analyze all your data, so results are consistent.

The whole pipeline of building up a sentiment analyser could be explained in following steps:

  1. Data Collection

  2. Prepare Your Data

  3. Create A Sentiment Analysis Model

  4. Visualize Your Results

 

Data Collection

Data could be collected from different sources based on the nature of the problem at hand for example in case of twitter sentiment analysis data could be added from twitter api tweepy. It’s important that your data is representative of what you’re trying to find out because you’ll use it to:

 

Train your sentiment analysis model

Test how your model performs on test set

Prepare Your Data

Once you’ve captured the data you need for your sentiment analysis, you’ll need to prepare your data. Social media data is unstructured. That means it’s raw, noisy, and needs to be cleaned before we can start working on our sentiment analysis model. This is an important step because the quality of the data will lead to more reliable results.

 

Preprocessing a textual dataset involves a series of tasks like removing all types of irrelevant information such as

  • emojis

  • special characters

  • extra blank spaces

  • hashtags

  • Special characters

  • stop words

 

It can also involve making format improvements, delete duplicate tweets, or tweets that are shorter than three characters

 

Choose the model

For this we can use different algorithms like SVM, DT, Random Forest, or neural networks and compare the evaluation matrix in order to understand which algorithm is better for our data.

 

Visualize Your Results

Data visualization tools help explain sentiment analysis results in a simple and effective way.

Spam Detection system Project Help

Fake users send undesired messages, mails, posts to users to promote services or websites that not only affect legitimate users but also disrupt resource consumption.The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable anti-spam filters. Machine learning techniques now days are used to automatically filter the spam e-mail in a very successful rate.

 

Few more prominent  Machine learning algorithms that could be applied to such problems are listed below:

  • Logistic Regression

  • Naïve Bayes

  • Stochastic Gradient Descent

  • K-Nearest Neighbours

  • Decision Tree

  • Random Forest

  • Support Vector Machine

There are many more such methods or algorithms which come under the label Supervised Machine Learning algorithms and could be used for Spam detection problems. Machine learning field is a subfield from the broad field of artificial intelligence, this aims to make machines able to learn like humans. Learning here means understanding, observing and representing information about some statistical phenomenon. In unsupervised learning one tries to uncover hidden regularities (clusters) or to detect anomalies in the data like spam messages or network intrusion. In e-mail filtering tasks some features could be the bag of words or the subject line analysis. Thus, the input to e-mail classification task can be viewed as a two dimensional matrix, whose axes are the messages and the features. E-mail classification tasks are often divided into several sub-tasks. First, Data collection and representation are mostly problem specific, second, e-mail feature selection and feature reduction attempt to reduce the dimensionality for the remaining steps of the task. Finally, the e-mail classification phase of the process finds the actual mapping between training set and testing set.

 

Some of the most successful methodologies available today in the field are mentioned below :

 

Artificial Neural Networks classifier

 

An artificial neural network (ANN), also called simply a "Neural Network" (NN), is a computational model based on biological neural networks. It consists of an interconnected collection of artificial neurons. An artificial neural network is an adaptive system that changes its structure based on information that flows through the artificial network during a learning phase. The ANN is based on the principle of learning by example.

 

Support Vector Machines classifier

 

Support Vector Machines are based on the concept of decision planes that define decision boundaries. A decision plane is one that separates between a set of objects having different class memberships, the SVM modeling algorithm finds an optimal hyperplane with the maximal margin to separate two classes.

 

 

Artificial Immune System classifier

 

Biological immune System has been successful at protecting the human body against a vast variety of foreign pathogens. The role of the immune system is to protect our bodies from infectious agents such as viruses, bacteria. On the surface of these agents are antigens that allow the identification of the invading agents, thus provoking an immune response Recognition in the immune system is performed by lymphocytes. Each lymphocyte expresses receptor molecules of one particular shape on its surface called antibodies. An elaborate genetic mechanism involving combinatorial association of a number of gene segments underlies the construction of these receptors. The overall immune response involves three evolutionary methods: gene library, negative selection and clonal selection. In gene libraries, antibodies recognize antigens by the complementary properties that belong only to antigens. Thus, some knowledge of antigen properties is required to generate competent antibodies. Because of this evolutionary self-organization process, in spam management the gene libraries act as archives of information on how to detect commonly observed antigens. An important constraint that the immune has to satisfy is not to attack self cells. Negative selection eliminates inappropriate antibodies which bind to self. Clonal selection clones antibodies performing well. Thus, according to currently existing antigens, only the fittest antibodies survive. Similarly, instead of having the information about specific antigens, it organizes the fittest antibodies by interacting with the current antigens.

 

Rough sets classifier

The Rough set scheme follows following steps:

Step 1: 

With the incoming emails, first thing we need to do is to select the most appropriate attributes to use for classification. Then the input dataset is transformed into a decision system , which is then split into the training dataset and the testing dataset. A classifier will be induced from the training dataset and applied to the testing dataset to obtain performance estimation. For training dataset, do Step 2 and Step 3.

 

Step 2: 

Because the decision system has real values attributes, Boolean reasoning algorithms should be used to finish the discretization strategies.

 

Step 3: 

Genetic algorithms should be used to get the decision rules. Then For testing dataset, continue to Step 4.

 

Step 4:

First, discretizes the testing dataset employing the same cuts computed from step 2. Then the rules generated in Step 3 are used to match every new object in the testing dataset to make a decision.


 

In case of spam detection the target variable contains the class labels which are listed below:

 

Spam - class

Spam is any kind of unwanted, unsolicited digital communication that gets sent out in bulk. And it's more than a nuisance. Spam today is a serious threat.

 

Non-Spam - class

This label represents all the non spam emails which are fine and can cause no threat.

 

Looking at these details, it comes under notice while looking at these algorithms is that all these algorithms are all supervised, this means there is always a target label available for us to train our model on.  Also when we look at details about the target class, their are only two classes. This shows that this problem could be represented as a binary classification problem. So what are binary classification problems? A short description of this is given below:

 

Binary Classification is one of the most common and frequently tackled problems in the machine learning domain. In it's simplest form the user tries to classify an entity into one of the two possible categories. For example, give the attributes of the fruits like weight, colour, peel texture, etc.

Survey Analysis Project Help

Surveys are an important way of evaluating a company’s performance. Companies conduct many surveys to get customer’s feedback on various products. This can be very useful in understanding the flaws and help companies improve their products

But, the problem arises when a lot of customers take the survey leading to increasing data size. It becomes impossible for a person to read them all and draw a conclusion. That’s where companies use natural language processing to analyze the surveys and generate insights from them, like knowing the sentiments of users about an event from the feedback and analyzing product reviews to understand the pros and cons. Today, most of the companies use these methods because they provide much more accurate and useful information.

 

Managing the advertising Funnel / Targeted Advertising

 

Targeted advertising works mainly on Keyword Matching. The Ads are associated with a keyword or phrase, and it is shown to only those users who search for the keyword similar to the keyword with which the advertisement was associated. 

What does your consumer need? Where is your consumer looking for his or her needs? Natural Language Processing is a great source for intelligent targeting and placement of advertisements in the right place at the right time and for the right audience. Reaching out to the right patron of your product is the ultimate goal for any business. NLP matches the right keywords in the text and helps to hit the right customers. Keyword matching is the simple task of NLP yet highly remunerative for businesses.

Adding interactive behavior to Text or Corpus

Steps to perform an action on to the text file

  • First, read the text file and then

  • Change these text file into the Tokens

  • Remove punctuation

  • Remove unusual words

  • Perform the operation on text

  • Now you read to get results

List of some important corpora used in NLP

Here there are some important corpora which used in NLP:

WordNet: Collectin of words and synonyms

Words: It is also a collection of words

Names: It is a collection of names, like male.txt and female.txt

TextBlob: It is also a good to work with NLP

SciPy:  It is demandable in current time.

Machine Translation

As the amount of information available online is growing, the need to access it becomes increasingly important and the value of natural language processing applications becomes clear.

Automatic summarization

Information overload is a real problem when we need to access a specific, important piece of information from a huge knowledge base.

What is NLP?

NLP is defined as a term of Artificial intelligence and machine learning because by the natural language we mean the language spoken by a human is making understandable by the machine. In this, we learn top NLP topics like,  how to tokenizing text, Text Identification, Gender Identification, Removing Unusual Words. 

 

Natural language processing involves the reading and understanding of spoken or written language through the medium of a computer. Through natural language processing, computers learn to accurately manage and apply overall linguistic meaning to text and understand the semantics behind the text.

Hire a NLP Programmers

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