Hertha  Walsh

Hertha Walsh

1602954000

The Stanford Sentiment Treebank (SST): Studying sentiment analysis using NLP

Published in 2013, “Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank” presented the Stanford Sentiment Treebank (SST). SST is well-regarded as a crucial dataset because of its ability to test an NLP model’s abilities on sentiment analysis. Let’s go over this fascinating dataset.

Predicting levels of sentiment from very negative to very positive (- -, -, 0, +, ++) on the Stanford Sentiment Treebank. Image credits to Socher et al., the original authors of the paper.

The task. SST handles the crucial task of sentiment analysis in which models must analyze the sentiment of a text. For example, this could come in the form of determining whether restaurant reviews are positive or negative. Here are some made-up examples that display a range of positivity and negativity in their sentiment:

This was the worst restaurant I have ever had the misfortune of eating at.

The restaurant was a bit slow in delivering their food, and they didn’t seem to be using the best ingredients.

This restaurant is pretty decent— its food is acceptable considering the low prices.

This is the best restaurant in the Western Hemisphere, and I will definitely be returning for another meal!

Based on these examples, sentiment analysis may seem like an easy task. However, there are lots of challenging nuances that can make it difficult to accurately analyze a phrase’s sentiment. Linguistic anomolies such as negation, sarcasm, and using negative terms in a positive way are especially difficult for NLP models to handle.

#ai #nlp #artificial-intelligence #machine-learning #data-science

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The Stanford Sentiment Treebank (SST): Studying sentiment analysis using NLP
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

Hertha  Walsh

Hertha Walsh

1602954000

The Stanford Sentiment Treebank (SST): Studying sentiment analysis using NLP

Published in 2013, “Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank” presented the Stanford Sentiment Treebank (SST). SST is well-regarded as a crucial dataset because of its ability to test an NLP model’s abilities on sentiment analysis. Let’s go over this fascinating dataset.

Predicting levels of sentiment from very negative to very positive (- -, -, 0, +, ++) on the Stanford Sentiment Treebank. Image credits to Socher et al., the original authors of the paper.

The task. SST handles the crucial task of sentiment analysis in which models must analyze the sentiment of a text. For example, this could come in the form of determining whether restaurant reviews are positive or negative. Here are some made-up examples that display a range of positivity and negativity in their sentiment:

This was the worst restaurant I have ever had the misfortune of eating at.

The restaurant was a bit slow in delivering their food, and they didn’t seem to be using the best ingredients.

This restaurant is pretty decent— its food is acceptable considering the low prices.

This is the best restaurant in the Western Hemisphere, and I will definitely be returning for another meal!

Based on these examples, sentiment analysis may seem like an easy task. However, there are lots of challenging nuances that can make it difficult to accurately analyze a phrase’s sentiment. Linguistic anomolies such as negation, sarcasm, and using negative terms in a positive way are especially difficult for NLP models to handle.

#ai #nlp #artificial-intelligence #machine-learning #data-science

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

Tia  Gottlieb

Tia Gottlieb

1594469520

Grouping Feedback: Pivot Tables and Sentiment Analysis using NLP

Background

Applying for product management internships is usually the same series of events. Link a resume, maybe a few fill in the blanks on a survey form, and submit. However, one of the most interesting PM applications I came across was IGN’s. Applicants were only given a set of questions to answer; no resume accepted. The process was great practice; this question was as follows…

“IGN has been collecting feedback from our wiki users to figure out ways we could improve their experience… Create a pivot table grouping this feedback into categories that will help us improve user experience on wiki pages, and what you would suggest for next steps.”

_Disclaimer: I assume this isn’t a breach of privacy, these questions are available on their application’s website _IGN Code Foo 2020


Overview

I decided to answer this question about grouping feedback from two angles (the lockdown gave me _a lot _of time to think); the straightforward “logical” approach using Excel and pivot tables, and the moonshot “creative” approach using machine learning & TensorFlow attempting to detect sentiment.

Dataset

Image for post

Feedback data from IGN’s Game Wiki pages


Logical Approach

Using Excel and creating a pivot table based on categories I felt were useful within the dataset.

Methodology

  • I started by visiting the URLs for each of the feedback to uncover some useful features and categories for grouping. It was only 24 sites, and from those visits, I built categories that lead to insightful filters for the data.

Image for post

Categorizing the feedback based on the best judgment

  • I’m not sure that was the most efficient way to scan webpages for content. I could have used an auto data scrape program, but live and learn.
  • I created a pivot table to better visualize the subsections found within the feedback. I used categories such as missing or wrong information, and I filtered data based on what I thought was most important, like country code top-level domain (ccTLD).

#sentiment-analysis #data analysis #data analysis

8 Open-Source Tools To Start Your NLP Journey

Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge. NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition techniques along with text simplification.

To address the current requirements of NLP, there are many open-source NLP tools, which are free and flexible enough for developers to customise it according to their needs. Not only these tools will help businesses analyse the required information from the unstructured text but also help in dealing with text analysis problems like classification, word ambiguity, sentiment analysis etc.

Here are eight NLP toolkits, in no particular order, that can help any enthusiast start their journey with Natural language Processing.


Also Read: Deep Learning-Based Text Analysis Tools NLP Enthusiasts Can Use To Parse Text

1| Natural Language Toolkit (NLTK)

About: Natural Language Toolkit aka NLTK is an open-source platform primarily used for Python programming which analyses human language. The platform has been trained on more than 50 corpora and lexical resources, including multilingual WordNet. Along with that, NLTK also includes many text processing libraries which can be used for text classification tokenisation, parsing, and semantic reasoning, to name a few. The platform is vastly used by students, linguists, educators as well as researchers to analyse text and make meaning out of it.


#developers corner #learning nlp #natural language processing #natural language processing tools #nlp #nlp career #nlp tools #open source nlp tools #opensource nlp tools