Sentiment Analysis Using TensorFlow Keras - Analytics India Magazine

Natural Language Processing is one of the artificial intelligence tasks performed with natural languages. The word ‘natural’ refers to the languages that evolved naturally among humans for communication. A long-standing goal in artificial intelligence is to make a machine effectively communicate with humans. Language modeling and Language generation (such as neural machine translation) have been popular among researchers for over a decade. For an AI beginner, learning and practicing Natural Language Processing can be initialized with classification of texts. Sentiment Analysis is among the text classification applications in which a given text is classified into a positive class or a negative class (sometimes, a neutral class, too) based on the context. This article discusses sentiment analysis using TensorFlow Keras with the IMDB movie reviews dataset, one of the famous Sentiment Analysis datasets.

TensorFlow’s Keras API offers the complete functionality required to build and execute a deep learning model. This article assumes that the reader is familiar with the basics of deep learning and Recurrent Neural Networks (RNNs). Nevertheless, the following articles may yield a good understanding of deep learning and RNNs:

• Getting Started With Deep Learning Using TensorFlow Keras
• Implementing A Recurrent Neural Network (RNN) From Scratch

#developers corner #imdb dataset #keras #lstm #lstm recurrent neural network #natural language processing #nlp #recurrent neural network #rnn #sentiment analysis #sentiment analysis nlp #tensorflow

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Sentiment Analysis Using TensorFlow Keras - Analytics India Magazine

Natural Language Processing is one of the artificial intelligence tasks performed with natural languages. The word ‘natural’ refers to the languages that evolved naturally among humans for communication. A long-standing goal in artificial intelligence is to make a machine effectively communicate with humans. Language modeling and Language generation (such as neural machine translation) have been popular among researchers for over a decade. For an AI beginner, learning and practicing Natural Language Processing can be initialized with classification of texts. Sentiment Analysis is among the text classification applications in which a given text is classified into a positive class or a negative class (sometimes, a neutral class, too) based on the context. This article discusses sentiment analysis using TensorFlow Keras with the IMDB movie reviews dataset, one of the famous Sentiment Analysis datasets.

TensorFlow’s Keras API offers the complete functionality required to build and execute a deep learning model. This article assumes that the reader is familiar with the basics of deep learning and Recurrent Neural Networks (RNNs). Nevertheless, the following articles may yield a good understanding of deep learning and RNNs:

• Getting Started With Deep Learning Using TensorFlow Keras
• Implementing A Recurrent Neural Network (RNN) From Scratch

#developers corner #imdb dataset #keras #lstm #lstm recurrent neural network #natural language processing #nlp #recurrent neural network #rnn #sentiment analysis #sentiment analysis nlp #tensorflow

Keras vs. Tensorflow - Difference Between Tensorflow and Keras

Keras and Tensorflow are two very popular deep learning frameworks. Deep Learning practitioners most widely use Keras and Tensorflow. Both of these frameworks have large community support. Both of these frameworks capture a major fraction of deep learning production.

Which framework is better for us then?

This blog will be focusing on Keras Vs Tensorflow. There are some differences between Keras and Tensorflow, which will help you choose between the two. We will provide you better insights on both these frameworks.

What is Keras?

Keras is a high-level API built on the top of a backend engine. The backend engine may be either TensorFlow, theano, or CNTK. It provides the ease to build neural networks without worrying about the backend implementation of tensors and optimization methods.

Fast prototyping allows for more experiments. Using Keras developers can convert their algorithms into results in less time. It provides an abstraction overs lower level computations.

Major Applications of Keras

• The performance of Keras is smooth on both CPU and GPU.
• Keras provides modularity, flexibility to code, extensibility, and has an adaptation for innovation and research.
• The pythonic nature of Keras makes it easy to explore and debug the code.

What is Tensorflow?

Tensorflow is a tool designed by Google for the deep learning developer community. The aim of TensorFlow was to make deep learning applications accessible to the people. It is an open-source library available on Github. It is one of the most famous libraries to experiment with deep learning. The popularity of TensorFlow is because of the ease of building and deployment of neural net models.

Major area of focus here is numerical computation. It was built keeping the processing computation power in mind. Therefore we can run TensorFlow applications on almost kind of computer.

Major applications of Tensorflow

• From mobiles to embedded devices and distributed servers Tensorflow runs on all the platforms.
• Tensorflow is the enterprise of solving real-world and real-time problems like image analysis, robotics, generating data, and NLP.
• Developers are implementing tools for translation languages and the detection of skin cancers using Tensorflow.
• Major projects using TensorFlow are Google translate, video detection, image recognition.

#keras tutorials #keras vs tensorflow #keras #tensorflow

Sentiment Analysis with TensorFlow Hub

In this article, I explained sentiment analysis with TensorFlow Hub using the IMDB dataset.

While performing data analysis, the model is trained with the training data and then inference on new data according to this trained model.

Model training is a difficult process. A large amount of labeling and computing power is required to train a model from scratch. For example, thousands of hours of GPU were run to train the NASNet model developed in 2017.

Fortunately, there are pre-trained models we can use for analysis such as text or image classification. These trained models are also available in libraries such as TensorFlow. To use these models, all you have to do is call these models and apply them to your datasets.

You may not get good results by applying these models directly to your datasets. Because these models are trained on certain data. For example, you want to classify pictures of dogs and cats. You want to use the ResNet model. ResNet is trained to classify thousands of images. You can only classify dog ​​and cat images by customizing the last layers of this model.

For more information on basic image classification, you can read the following blog post.

#machine-learning #sentiment-analysis #deep-learning #artificial-intelligence #tensorflow-hub #sentiment analysis with tensorflow hub

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

AI & Data Science India Salary Study - 2021

The annual Analytics India Salary report presented by AIM and AnalytixLabs is the only annual study in India that delves into salary trends and provides a comprehensive view of the changing landscape of analytics salaries. The report, now in its seventh year, look at the distribution of average salaries across several categories including years of experience, metropolitan regions, industries, education levels, gender, tools, and skills.

The Data Analytics function is experiencing significant growth and development in terms of skills, capabilities, and funding. Last year, despite the pandemic, the Indian start-up industry witnessed $836.3 million investment, almost a 10% (9.7%) increase than the previous year. Also, more than one in five (21%) analytics teams across firms in India witnessed a growth in the last 12 months and the post-pandemic job market saw an upswing of data science jobs. The development of the data science domain is evidenced by the high salaries drawn by analytics professionals across the organization, with Analytics professionals doing relatively well in spite of the pandemic.

#featured #ai salaries in india #analytics salaries in india #analytics salary key trends #analytics salary trend #average data analytics salary #average salary of analytics professionals #data science salaries in india #data science salary study #latest data science salaries