(testing signal)

Tag: SentimentAnalysis

Crypto Market Sentiment Down for Third Week in a Row

Source: iStock/Koonsiri Boonnak Crypto market sentiment dropped for the third week in a row, now standing quite close to the bottom line of the 5-6 range. The average 7-day moving crypto market sentiment score (sentscore) for ten major cryptoassets is 5.18 today, whereas it was 5.22 a week ago, according to data provided by the market sentiment analysis service Omenics.Only four of the ten coins are in green, with tether (USDT) taking the lead once again, having risen 6% over the past seven days. Right behind it is algorand…

An App Builder for the Data Science Team – The New Stack

Not only are companies collecting vast amounts of data, but new types of data like geographic data and sentiment analysis that’s being used to not only chart the past but, with machine learning, to predict the future.
Yet companies haven’t been able to take full advantage of the data they have because sharing it internally took too much time and human resources to build the kind of applications to fully harness the data.
Enter Streamlit, an open source framework making it easy for data scientists to quickly build web apps to access and explore machine learning models, advanced…

Crypto Market Sentiment Drops

Source: iStock/da-kukDespite barely clinging to the positive zone last Monday, crypto market sentiment has taken a hit over the past week. Compared to last week’s 6.02, the average 7-day moving crypto market sentiment score (sentscore) for ten major cryptoassets is 5.47 today, according to data provided by the market sentiment analysis service Omenics.Crypto.com coin (CRO), is the week’s winner as it remained almost unchanged. The highest losses are seen by XRP and cardano (ADA), the sentscores of which fell over 15%. But other drops are notable too. Polkadot (DOT)’s sentscore fell…

ML Algorithms in Online User Reviews for Sentiment Analysis

The online ecosystem is designed to be open for live interactions. Online users can indulge in immersive web pages, do social conversations, and post online reviews; the web platforms are built to encourage them to post opinions without restrictions. This has stretched the scope for building several meaningful digital experiences in the web ecosystem.Online products and services get positive and negative reviews, every now and then. A fair amount of scrutinizing behavior by users can be…

Analyzing the Imperatives in Textual Sentiment Analysis

Sentiment AnalysisSentiment analysis is a technique for determining people’s thoughts, sentiments, and emotions regarding a product or service. It is, in theory, a computational analysis of text-based opinions, feelings, attitudes, perspectives, emotions, and so on. This content can take several forms, including reviews, blogs, news, and comments. The capacity to derive insights from this sort of data is a technique that many companies across the world have adopted. It has a wide range of…

Real-Time Stock News Sentiment Analyzer

Natural Language ProcessingInvesting in the Stock Market is a great way of tackling Inflation. Inflation refers to the rise in the prices of most goods and services of daily or common use, such as food, clothing, housing, recreation, transport, consumer staples, etc. Basically, with 100 rupees you won’t be able to buy as many vada pavs (wadapavs) as you could last year.In the pandemic-struck financial year of 2020–2021 a whopping 142 lakh new investors have started trading in the…

Applications of Sentiment Analysis

What is Sentiment Analysis?
Sentiment analysis can be defined as analyzing the positive or negative sentiment of the customer in text. The contextual analysis of identifying information helps businesses understand their customers’ social sentiment by monitoring online conversations. 
1. Brand Monitoring 
A brand is not defined by the product it manufactures. It depends on how you build a brand by online marketing, social campaigning, content marketing, and customer support services….

Multilayer Perceptron Explained with a Real-Life Example and Python Code: Sentiment Analysis

This series of articles focuses on Deep Learning algorithms, which have been getting a lot of attention in the last few years, as many of its applications take center stage in our day-to-day life. From self-driving cars to voice assistants, face recognition or the ability to transcribe speech into text.


Build a machine learning web app in Python

We are going to build a simple sentiment analysis application. This app will get a sentence as user input, and return with a prediction of whether this sentence is positive, negative, or neutral.

Here’s how the end product will look:

Image by author

You can use any Python IDE to build and deploy this app. I suggest using Atom, Visual Studio Code, or Eclipse. You won’t be able to use a Jupyter Notebook to build the web application. Jupyter Notebooks are built primarily for data analysis, and it isn’t feasible to run a web server with Jupyter.

Once you have a programming IDE set up, you will need to have the following libraries installed: Pandas, Nltk, and Flask.

Create a folder for this entire sentiment analysis project. Under your main folder, you will need to have the following files:

Image by author

Let’s go through these files one by one:

First, create the file called main.py


Is Data Science Practically Useful?

Despite being in the profession for years, it took me some time to get out of the if-then mindset of my day-to-day and begin to analyze how data science could be useful to me (not just the companies I have helped to apply it).

It all started with a pause in my day to take stock of the tasks that were taking up most of my time.

My foray into practical data science all started with an inordinate amount of time spent arguing about politics with my family and extended Facebook friends. I found myself looking for data comparing extreme right and extreme left politics, to point out that the two are simply not the same.

But my anecdotes and hard-to-find research articles only seemed to fall on deaf ears. So, to save myself time, a lot of frustration, and arm myself with data I turned my time to data science.


An Automatic Hyperparameter Optimization on a Twitter Sentiment Analysis Problem

Optuna Integration

Now, we are ready to train the model and tune the hyperparameters. Install Optuna by:

pip install optuna

In the following code, you will notice an objective function that is being optimized by Optuna. Firstly, we define the hyperparameters that we are interested in tuning and add them to the trial object. Here, I chose to tune learning_rate, max_depth and n_estimators . Depending on the type of hyperparameter, we can use methods such as suggest_float, suggest_int, suggest_categorical .

Inside this objective function, we create an instance of the model and fit it on the training set. After training, we predict the sentiment on the validation set and calculate the accuracy metric. The Optuna’s objective function will try to maximize this accuracy score by performing trials with different values of hyperparameters.


Take it to Twitter: Social Media Analysis of Members of Congress

Understanding Views of Congress on Social and Policy Issues through Sentiment Analysis in R

Image by Author, Portraits from Government Printing Office

Twitter and American Politics

Since the Trump administration took office, Twitter usage has been discussed ad nauseam in American politics. While Former President Trump has since left office, the importance of the social media app has remained constant. Twitter has now become a primary way for congressional members to engage with their constituents and express their opinions on policy issues.

With the new Biden Administration taking office, congressional bipartisanship has been at the forefront of every major issue in politics. With tight margins in both the House and the Senate, the need for members of both parties to support legislation is crucial for effective government.


Top Use Cases of Natural Language Processing in Healthcare

Better access to data-driven technology as procured by healthcare organisations can enhance healthcare and expand business endorsements. But, it is not simple for the company enterprise systems to utilise the many gigabytes of health and web data. But, not to worry, the drivers of NLP in healthcare are a feasible part of the remedy.

What is NLP in Healthcare? 

The NLP illustrates the manners in which artificial intelligence policies gather and assess unstructured data from the language of humans to extract patterns, get the meaning and thus compose feedback. This is helping the healthcare industry to make the best use of unstructured data. This technology facilitates providers to automate the managerial job, invest more time in taking care of the patients, and enrich the patient’s experience using real-time data.


How To Train A Deep Learning Sentiment Analysis Model

How to train your own high performing sentiment analysis model

Photo by Pietro Jeng on Unsplash

Sentiment analysis is a technique in natural language processing used to identify emotions associated with the text. Common use cases of sentiment analysis include monitoring customers’ feedbacks on social media, brand and campaign monitoring.

In this article, we examine how you can train your own sentiment analysis model on a custom dataset by leveraging on a pre-trained HuggingFace model. We will also examine how to efficiently perform single and batch prediction on the fine-tuned model in both CPU and GPU environments. If you are looking to for an out-of-the-box sentiment analysis model, check out my previous article on how to perform sentiment analysis in python with just 3 lines of code.


Exploring BERT Language Framework for NLP Tasks

As artificial intelligence apes the human speech, vision, and mind patterns, the domain of NLP is buzzing with some key developments in place.

NLP is one of the most crucial components for structuring a language-focused AI program, for example, the chatbots which readily assist visitors on the websites and AI-based voice assistants or VAs. NLP as the subset of AI enables machines to understand the language text and interpret the intent behind it by various means. A hoard of other tasks is being added via NLP like sentiment analysis, text classification, text extraction, text summarization, speech recognition, and auto-correction, etc.

However, NLP is being explored for many more tasks. There have been many advancements lately in the field of NLP and also NLU (natural language understanding) which are being applied on many analytics and modern BI platforms.