An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

This is demo repo to demostrate how to scrape post data from Facebook by Python with library facebook_scraper. And then use Azure Text Analytics to perform sentiment analysis for post text content.

This repository helps Data Analytics/Science Enthusiasts to carry out Sentiment Analysis on text derived from Twitter, Facebook and other data sources.

Sentiment Analysis with Insights using NLP and Dash This project show the sentiment analysis of text data using NLP and Dash. I used Amazon reviews dataset to train the model and further scrap the reviews from Etsy.com in order to test my model. Prerequisites: Python3 Amazon Dataset (3.6GB) Anaconda How this project was made? This project has been built using Python3 to help predict the sentiments with the help of Machine Learning and an interactive dashboard to test reviews. To start, I downloaded the dataset and extracted the JSON file. Next, I took out a portion of 7,92,000 reviews equally distributed into chunks of 24000 reviews using pandas. The chunks were then combined into a single CSV file called balanced_reviews.csv. This balanced_reviews.csv served as the base for training my model which was filtered on the basis of review greater than 3 and less than 3. Further, this filtered data was vectorized using TF_IDF vectorizer. After training the model to a 90% accuracy, the reviews were scrapped from Etsy.com in order to test our model. Finally, I built a dashboard in which we can check the sentiments based on input given by the user or can check the sentiments of reviews scrapped from the website. What is CountVectorizer? CountVectorizer is a great tool provided by the scikit-learn library in Python. It is used to transform a given text into a vector on the basis of the frequency (count) of each word that occurs in the entire text. This is helpful when we have multiple such texts, and we wish to convert each word in each text into vectors (for using in further text analysis). CountVectorizer creates a matrix in which each unique word is represented by a column of the matrix, and each text sample from the document is a row in the matrix. The value of each cell is nothing but the count of the word in that particular text sample. What is TF-IDF Vectorizer? TF-IDF stands for Term Frequency - Inverse Document Frequency and is a statistic that aims to better define how important a word is for a document, while also taking into account the relation to other documents from the same corpus. This is performed by looking at how many times a word appears into a document while also paying attention to how many times the same word appears in other documents in the corpus. The rationale behind this is the following: a word that frequently appears in a document has more relevancy for that document, meaning that there is higher probability that the document is about or in relation to that specific word a word that frequently appears in more documents may prevent us from finding the right document in a collection; the word is relevant either for all documents or for none. Either way, it will not help us filter out a single document or a small subset of documents from the whole set. So then TF-IDF is a score which is applied to every word in every document in our dataset. And for every word, the TF-IDF value increases with every appearance of the word in a document, but is gradually decreased with every appearance in other documents. What is Plotly Dash? Dash is a productive Python framework for building web analytic applications. Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It's particularly suited for anyone who works with data in Python. Dash apps are rendered in the web browser. You can deploy your apps to servers and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready. Dash is an open source library, released under the permissive MIT license. Plotly develops Dash and offers a platform for managing Dash apps in an enterprise environment. What is Web Scrapping? Web scraping is a term used to describe the use of a program or algorithm to extract and process large amounts of data from the web. Running the project Step 1: Download the dataset and extract the JSON data in your project folder. Make a folder filtered_chunks and run the data_extraction.py file. This will extract data from the JSON file into equal sized chunks and then combine them into a single CSV file called balanced_reviews.csv. Step 2: Run the data_cleaning_preprocessing_and_vectorizing.py file. This will clean and filter out the data. Next the filtered data will be fed to the TF-IDF Vectorizer and then the model will be pickled in a trained_model.pkl file and the Vocabulary of the trained model will be stored as vocab.pkl. Keep these two files in a folder named model_files. Step 3: Now run the etsy_review_scrapper.py file. Adjust the range of pages and product to be scrapped as it might take a long long time to process. A small sized data is sufficient to check the accuracy of our model. The scrapped data will be stored in csv as well as db file. Step 4: Finally, run the app.py file that will start up the Dash server and we can check the working of our model either by typing or either by selecting the preloaded scrapped reviews.

This is demo repo to demostrate how to scrape apps review data from Google Play Store by Python with library Google-Play-Scraper. And then use Azure Text Analytics to perform sentiment analysis for reviews content (aka comments).

Slackmetrics is a analytical product for Slack's data. IT also include three text mining algorithms for: sentiment analysis, insult detection and categories.

This repository contains a hands on lab for building a bot framework bot that connects to the Microsoft Graph to search mail and perform sentiment analysis on messages (using the Microsoft Text Analytics cognitive service).

Production-ready AI Text Analytics API with sentiment analysis, language detection, content generation, AI detection and more

In this project we will be classifying reviews given by the employers for the employee or the company as positive or negative reviews. The dataset contains 67,529 rows and 15 columns. The dataset has information primarily regarding the company, position, date, pros and cons. This project can help a company analyze the ratio of employees that are satisfied or not satisfied with their work environment. This can help in future improvements and help give a better experience to the future employees. Using the word cloud for positive and negative sentiment, they can better understand which problems are more precarious than the others and focus on them rather than those that don’t need immediate attention. This can also be leveraged by rival companies to understand the problems of the competition to avoid those themselves. The positive views can also be used extensively to understand why the competition may be prospering and can be incorporated into a company’s work culture for a holistic work experience. Using sentiment analysis on reviews of any kind can help in understanding the deep-seated issues with a product or a workplace and can also be used to optimize on all the things that are going right and strive towards excellence. Steps: Data collection: the first step of sentiment analysis consists of collecting data from user generated content contained in blogs, forums, social networks and text analytics and natural language processing are used to extract and classify. In our case it is collected from Kaggle. Text preparation: consists of cleaning the extracted data before analysis. We will be using techniques such as bag of words and lemmatization. Feature Extraction: the extracted sentences of the reviews and opinions are examined. Use word embedding (count vectorizer, tf-idf transformation, Word2Vec) to transform reviews into numerical representations. Machine learning classifier: Fit numerical representations of reviews to machine learning algorithms. We will be using Naïve Bayes, Logistic Regression, Random Forest and LSTM. Sentiment classification: Subjective sentences are classified in positive, negative, good or bad. Presentation of output: the main objective of sentiment analysis is to convert unstructured text into meaningful

Video Analytics in Python using face-emotion-detection, speech-to-text and text-sentiment analysis pre-trained DEEP LEARNING models

Advanced Turkish NLP pipeline using BERT for sentiment analysis and topic extraction. Features dual-method sentiment analysis, semantic clustering, and ChatGPT API integration for comprehensive Turkish text analytics.

We would like you to track and analyse the election chatter that happens in twitter, facebook and other social media channels, news sites and portals. Analyse trends and patterns and even predict the outcome of these elections. Choose a problem within the domain of 2018 assembly elections. For example, you may use different data collection methods as needed and collect different opinions from influencers and key opinion leaders on social media and analyse the sentiment of the voters. Or, you may choose to check the veracity of the opinion poll and exit poll data done by popular news channels by applying statistical concepts learnt. Whatever the problem you pick within the bounds of assembly elections, you are expected to leverage data visualization techniques learnt in the class room. Explore the data using visualization and do the first cut analysis and then deeper analysis. Apply text analytics to do various NLP tasks that help you derive election insights from social media and beyond. You can also run “Google Trends” to see the relevant trends on different elections for different time periods. Incorporate the trends in conjunction with the chatter from the media and do text analytics. Even you may do some big data analysis. You are welcome to choose any publicly available dataset of tweets, trends and posts. These questions are to generate curiosity in you.

An interdisciplinary study observing conversations of diversity in technology through a combination of close readings of individual texts; analytics including sentiment analysis, text clustering, and lexical analysis; and visualizations, we seek to reveal features of these texts’ vocabularies, rhetorical and affective strategies, and semantic patterns. We will remain alert to any evidence of potential conscious and unconscious bias, relationships, or patterns within and between the corpora on Twitter.

Sentiment analysis also known as opinion mining is a subfield within Natural Language Processing (NLP) that builds machine learning algorithms to classify a text according to the sentimental polarities of opinions it contains, e.g., positive or negative. In recent year, sentiment analysis has become a topic of great interest and development in both academics and industry. Analysing the sentiment of texts could benefit, for example, customer services, product analytics, market research etc. Take Ebay as an example. Customers on Ebay choose their preferred products based on the reviews from other users. an automatic sentiment classification system can not only help companies grasp the satisfaction level of the products, but also significantly assist new customers to locate their online shopping shelves. In this data analysis challenge, we are interested in developing such an automatic sentiment classification system that relies on machine learning techniques to learn from a large set of product reviews provided by Yelp. The levels of polarity of opinion we consider include strong negative, weak negative, neutral, weak positive, and strong positive. For example, “Website says open, Google says open, Yelp says open on Sundays. Our delivery was cancelled suddenly and no one is answering the phone. Shame” gives us a strong negative sentiment, whereas the sentiment of “They have great food & definitely excellent service. Tried their mochi mango flavored and it s definitely delis” is likely to be strong positive. The sentiment analysis task is often formulated as a classification problem, where a classifier is fed with a text and returns the corresponding sentiment label, e.g., positive, negative, or neutral. In other words, the problem of learning the sentimental polarities of opinions is reduced to a classi- fication problem. There are many machine learning methods that can be used in the classification task. They can be categorised into supervised method (like SVM) and unsupervised method (like clustering).

Sentiment analysis and opinion mining with the Azure Text Analytics client library

Sentiment Analysis blog article sample with Azure Text Analytics API and .NET Core 2.1

All things related to Text Analytics - Sentiment Analysis, Topic Modeling, TF-IDF, GloVe and Word2Vec Algos.

A Natural Language Processing app for Sentiment, Key Phrases and Similarity Analysis using "Webit Text Analytics" API for backend.

A Python-based Text Analytics and Sentiment Analysis tool that extracts web article content and computes various linguistic, readability, and sentiment metrics including polarity, subjectivity, Fog Index, and pronoun usage.

End-to-end Power BI solution that integrates Azure AI Language (Text Analytics) for sentiment analysis in Spanish reviews. Includes sample dataset, M scripts, DAX measures, and best-practice guide for secure and scalable AI-assisted Business Intelligence.

AnalytiQ is an AI-driven analytics platform designed to extract key insights from emails, surveys, and text data built for the Microsoft hackathon, it leverages Azure AI, Power BI, and .NET to deliver enterprise-grade data visualization and sentiment analysis.

This is a comprehensive AI-powered sentiment analysis platform that combines machine learning, web technologies, and advanced NLP features to analyze news articles and text content with 86% accuracy. The project has evolved into a full-featured platform with real-time analytics, multilingual support, and extensive integration capabilities.

This repository contains the code files and report for the project "Unleashing the Power of Large Language Models: GPT-3.5 and BERT versus Traditional Models for Sentiment Analysis in Airbnb Review" developed as a final project in the course Natural Language Processing and Text Analytics at the Copenhagen Business School.

Sentiment analysis is a technology of increasing importance in the modern society as it allows individuals and organizations to detect trends in public opinion by analyzing social media content

A comprehensive Text Analytics & Sentiment Analysis project featuring tokenization, stemming, POS tagging, syntactic parsing, bigram modelling, supervised classification, and model evaluation. Includes Python implementations, EDA, multiple ML models, hyperparameter tuning, and detailed analysis of predictive performance.

Classifying whether tweets are hatred-related tweets or not using CountVectorizer and Support Vector Classifier in Python

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Using Text Analytics on Amazon Reviews in R for Sentiment and Product Analysis

Classification Models - Naive Bayes and KNN, Decision Tree, Ensemble Techniques, Random Forests, Bagging, Boosting, Text Analytics and Sentiment Analysis

SF Alcohol Consumption Text Analytics, Sentiment Analysis, Word Cloud and Contribution, N-Grams, Bigam Network, Pairwise Correlations, Prediction and Shiny Presentation.