Mia  Marquardt

Mia Marquardt

1622218440

Most Benchmarked Datasets in Neural Sentiment Analysis With in PyTorch and TensorFlow

With the expanding prominence of blogging sites, a massive number of clients share reviews on various parts of life consistently. Therefore popular sites like Amazon, Twitter are rich wellsprings of information for opinion mining and sentiment analysis.

Sentiment analysis is a technique in natural language processing that deals with the order of assessments communicated in a bit of text. In other words, it is used to check the polarity of the sentences.

Sentiment analysis approach utilises an AI approach or a vocabulary based way to deal with investigating human sentiment about a point. The test for sentiment investigation lies in recognizing human feelings communicated in this content, for example, Twitter information.

Here, our focus will be to cover the details of some of the most popular datasets used in sentiment analysis. Further,we will focus on executing the code on these datasets using Tensorflow and Pytorch.

#developers corner #datasets #natural language processing #neural sentiment analysis #opinion mining #pytorch #sentiment analysis

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Most Benchmarked Datasets in Neural Sentiment Analysis With in PyTorch and TensorFlow
Mia  Marquardt

Mia Marquardt

1622218440

Most Benchmarked Datasets in Neural Sentiment Analysis With in PyTorch and TensorFlow

With the expanding prominence of blogging sites, a massive number of clients share reviews on various parts of life consistently. Therefore popular sites like Amazon, Twitter are rich wellsprings of information for opinion mining and sentiment analysis.

Sentiment analysis is a technique in natural language processing that deals with the order of assessments communicated in a bit of text. In other words, it is used to check the polarity of the sentences.

Sentiment analysis approach utilises an AI approach or a vocabulary based way to deal with investigating human sentiment about a point. The test for sentiment investigation lies in recognizing human feelings communicated in this content, for example, Twitter information.

Here, our focus will be to cover the details of some of the most popular datasets used in sentiment analysis. Further,we will focus on executing the code on these datasets using Tensorflow and Pytorch.

#developers corner #datasets #natural language processing #neural sentiment analysis #opinion mining #pytorch #sentiment analysis

Dominic  Feeney

Dominic Feeney

1622273248

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:

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

Datasets in Neural Sentiment Analysis With Implementation in PyTorch and TensorFlow

With the expanding prominence of blogging sites, a massive number of clients share reviews on various parts of life consistently. Therefore popular sites like Amazon, Twitter are rich wellsprings of information for opinion mining and sentiment analysis.
Read more: https://zcu.io/oZVF

#pytorch #tensorflow #sentimentanalysis #datasets

Mia  Marquardt

Mia Marquardt

1624077346

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

Sofia  Maggio

Sofia Maggio

1626077565

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