Pybot can change the way learners try to learn python programming language in a more interactive way. This chatbot will try to solve or provide answer to almost every python related issues or queries that the user is asking for. We are implementing NLP for improving the efficiency of the chatbot. We will include voice feature for more interactivity to the user. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. NLTK has been called “a wonderful tool for teaching and working in, computational linguistics using Python,” and “an amazing library to play with natural language.The main issue with text data is that it is all in text format (strings). However, the Machine learning algorithms need some sort of numerical feature vector in order to perform the task. So before we start with any NLP project we need to pre-process it to make it ideal for working. Converting the entire text into uppercase or lowercase, so that the algorithm does not treat the same words in different cases as different Tokenization is just the term used to describe the process of converting the normal text strings into a list of tokens i.e words that we actually want. Sentence tokenizer can be used to find the list of sentences and Word tokenizer can be used to find the list of words in strings.Removing Noise i.e everything that isn’t in a standard number or letter.Removing Stop words. Sometimes, some extremely common words which would appear to be of little value in helping select documents matching a user need are excluded from the vocabulary entirely. These words are called stop words.Stemming is the process of reducing inflected (or sometimes derived) words to their stem, base or root form — generally a written word form. Example if we were to stem the following words: “Stems”, “Stemming”, “Stemmed”, “and Stemtization”, the result would be a single word “stem”. A slight variant of stemming is lemmatization. The major difference between these is, that, stemming can often create non-existent words, whereas lemmas are actual words. So, your root stem, meaning the word you end up with, is not something you can just look up in a dictionary, but you can look up a lemma. Examples of Lemmatization are that “run” is a base form for words like “running” or “ran” or that the word “better” and “good” are in the same lemma so they are considered the same.

Customer reviews sentiment analysis with Python and NLP. Generates a synthetic dataset of positive, neutral, and negative reviews, applies preprocessing (tokenization, stopwords, lemmatization), and builds TF-IDF features. Trains classifiers (Naive Bayes, Logistic Regression, Random Forest) with evaluation, confusion matrix and top features.

A collection of Python-based NLP projects exploring text processing, sentiment analysis, and language modeling using libraries like NLTK, SpaCy, and Transformers. Includes hands-on implementations with datasets and tutorials for building and evaluating NLP models.

The project is a simple sentiment analysis using NLP. The project in written in python with Jupyter notebook. It shows how to do text preprocessing (removing of bad words, stop words, lemmatization, tokenization). It further shows how to save a trained model, and use the model in a real life suitation. The machine learning model used here is k-Nearest Neighbor which is used to build the model. Various performance evaluation techniques are used, and they include confusion matrix, and Scikit-learn libraries classification report which give the accuracy, precision, recall and f1- score preformance of the model. The target values been classified are positive and negative review.

Text classification is a fundamental task in natural language processing (NLP), used widely for spam detection, sentiment analysis, and categorization of textual data. In this Python script, we delve into building a text classification pipeline using a Naive Bayes classifier with TF-IDF (Term Frequency-Inverse Document Frequency) features.

Sentiment Analysis Python script using NLP (NLTK's VADER model) tool that analyses text data and labels them with sentiment scores.

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.

A basic NLP pipeline in Python with time-series sentiment analysis

Overview Virtual Assistant is an application program that understands natural language voice commands or text commands and completes the tasks for users. Virtual Assistants features a human interface system, they can understand the language and meaning of what the user is saying and have built in replies. Learn from different instances so that they can have a long term human interaction. It uses artificial intelligence to learn things from different situations. Using AI they can recognize, predict and classify based on analysis. Purpose Virtual Assistant provides various services. It is ready to help wherever you are and can be deployed in your devices. Wider scope and perform users to get answers to their questions and perform tasks using voice or text commands, all in an interactive form. Precise voice and text recognition with the ability to have conversation with the users. In case of Google assistant, they recognize the voice of the user and perform the specific task. Use case Customer support: Rather of customers waiting for a long to solve an issue, the can get instant support from chatbot, Banking Chatbots: Personalized banking with an aim to improve customer satisfaction and engagement. Project support: Can send notifications for various tasks. Reminder to follow up with an action. HR assistants: Can help employees register time off, retrieve company policies, and find answers to repetitive employment questions. Teaching: Can helps teachers to create more detailed learning plans and materials. Being full-blown health assistants: Virtual assistants can do so much more than giving tips, they can often help patients apply simple treatments, remind them to take medicine, and monitor their health. Automating FAQs and administrative tasks: If there's a scenario where the customers have dozens of repetitive questions, virtual assistant is there 24/7 to answer questions from people who may be anxious to get answers. Technical support: The customer has a product technical error, in this case, asks the customer to type the error they encounter, then it generates a dynamic link to search the customer input words in the technical knowledge repositories and guide the customer through his search. Efficient Processes: Make processes more streamlined and transparent by synchronizing between functions, roles, and departments. Booking: A virtual assistant can respond to a consumer through messages, web, SMS or email and update them on the status of their existing reservation, make changes to the reservation, process related payments or refunds, send proactive notifications and provide detailed information on their itinerary. Features a. NLP Text Search : Virtual assistant concentrates on NLP and NLU. Understands the slang that is used in everyday conversation and analyses the sentiments to enhance a better set of communication. b. FAQ voice assistant : FAQ voice assistant is a voice assistant that provides a list of questions and answers relating to a particular subject. c. Conversations voice assistant : Conversations voice assistant is a voice assistant that provides conversational services based on a subject. d. Speech conversations (STT,TTS) : It provides conversational services such as speech to text and text to speech. e. Integration with Enterprise Systems : It provides administrative service to clients. Such as scheduling appointments, making phone calls, making travel arrangements, managing email accounts etc. f. Rich Conversations : Rich conversation is a conversation that can use different features such as images, videos, buttons, forms etc. a) Images:Imagescanbesentorreceivedduringconversations. b) Buttons:Buttonscanprovidedifferentfunctionalitiesasperthefeatureofthebutton. c) Videos:Videoscanbesentorreceivedduringconversations d) Forms: Forms help to give visible shape or configuration of something. Technical Requirement g. HTML5 h. JavaScript i. Python (Flask API, NLP Packages) j. MySQL k. Docker l. Git

Natural language processing (NLP) is an exciting branch of artificial intelligence (AI) that allows machines to break down and understand human language. I plan to walk through text pre-processing techniques, machine learning techniques and Python libraries for NLP. Text pre-processing techniques include tokenization, text normalization and data cleaning. Once in a standard format, various machine learning techniques can be applied to better understand the data. This includes using popular modeling techniques to classify emails as spam or not, or to score the sentiment of a tweet on Twitter. Newer, more complex techniques can also be used such as topic modeling, word embeddings or text generation with deep learning. We will walk through an example in Jupyter Notebook that goes through all of the steps of a text analysis project, using several NLP libraries in Python including NLTK, TextBlob, spaCy and gensim along with the standard machine learning libraries including pandas and scikit-learn.

Python Jupyter Notebook that reads in 10,000 Amazon Product Reviews and then performs an NLP Sentiment Analysis on the data with both the VADER Model and Roberta pretrained model, and then comparing the two models .

I employed NLP techniques to evaluate user feedback on ChatGPT, utilizing Python libraries like VADER for sentiment analysis to categorize reviews into positive, neutral, and negative sentiments. Implemented data preprocessing techniques such as tokenization and stopword removal, visualizing results with Plotly to yield actionable insights.

Automate cryptocurrency news sentiment analysis using Python. Scrape news, analyze sentiment with NLP tools (VADER/TextBlob), and visualize trends to help understand market sentiment.

My Python-based NLP app includes sentiment analysis, named entity recognition, and emotion detection, with login/signup and JSON authentication.

Built a Flask web application of TripAdvisor attraction data analyzer, the user can enter the URL link of an attraction on TripAdvisor and have the review data analyzed instantly with sentiment analysis, built with Python, selenium and NLP

n this Omdena project, our goal was to develop open-source Python Arabic NLP libraries that the Arab world will easily use in all Natural Language Processing applications like Morphological analysis, Named Entity Recognition, Sentiment Analysis, Word Embedding, Dialect Identification, Part of speech, and so on the training dataset. This article contains interesting code and could be beneficial for whatever your level of experience, but for beginners, it is a great start-up in data collection using web scraping with referral links to official documentation pages for every mentioned library.

A repository for learning sentiment analysis with Python, blending theory and code. It introduces sentiment analysis fundamentals, NLP techniques, and machine learning algorithms for sentiment detection in texts. Includes tutorials and Python code examples for hands-on learning.

A Bangla NLP application with python interactive gui for Bengali text summerization, sentiment analysis and word cloud generation.

This project implements a sentiment analysis application using Natural Language Processing (NLP) techniques. Built with Python and Flask, it classifies text into positive, negative, or neutral sentiments.

📌 an interactive Discord bot built with Python , It features sentiment analysis with NLTK, understanding the user's prompt using NLP , and currenlty using Wekipedia + Google Model's API for queries

A Machine Learning-based Book Recommendation System that suggests books using content-based filtering and mood analysis. It leverages TF-IDF vectorization, cosine similarity, and NLP sentiment analysis to provide personalized recommendations. Built with Python, Scikit-Learn, Pandas, and NLP tools. 🚀

This project analyzes YouTube video comments using Python to classify them into positive, negative, neutral, and query-based sentiments. It uses NLP techniques, visualizes data with Seaborn, and includes features like comment length detection and video URL input for user-friendly analysis.

NLP Sentiment Analysis with Naïve Bayes Classifier built in Python without using any libraries.

No description available

Sentiment Analysis with NLP using Python and Flask

Sentiment Analysis App with NLP using Python and Flask

Senticome – Amazon-style e-commerce with built-in NLP sentiment analysis. React • Node • MongoDB • Python.

Natural Language Processing (NLP) on twitter data using roBERTa model with python for sentiment analysis.

Modern hotel review sentiment analysis with interactive GUI, AI explanations, and educational features. Python/ML/NLP project.

NLP techniques such as named entity recognition, sentiment analysis, topic modeling, text classification with Python to predict sentiment and rating of drug from user reviews.