It is a Natural Language Processing Problem where Sentiment Analysis is done by Classifying the Positive tweets from negative tweets by machine learning models for classification, text mining, text analysis, data analysis and data visualization

An implementation in TensorFlow of a convolutional neural network (CNN) to perform sentiment classification on tweets.

End-to-end sentiment analysis of tweets using BERT. Includes preprocessing, training, and evaluation with classification reports, confusion matrices, ROC curves, and word clouds. Demonstrates fine-tuning of transformer models for text classification with modular, reproducible code.

In this project, I extend the implementation of a Tweet/Sentence Sentiment Classification to a "Depression Assistant Chatbot". This software asks the users how they are feeling and if what they write expresses sadness of anger then they are greeted with jokes until they feel better.

Corona Virus Sentiment Analysis.This challenge asks you to build a classification model to predict the sentiment of COVID-19 tweets.The tweets have been pulled from Twitter and manual tagging has been done then.

Twitter tweets play an important role in every organisation. This project is based on analysing the English tweets and categorizing the tweets based on the sentiment and emotions of the user. The literature survey conducted showed promising results of using hybrid methodologies for sentiment and emotion analysis. Four different hybrid methodologies have been used for analysing the tweets belonging to various categories. A combination of classification and regression approaches using different deep learning models such as Bidirectional LSTM, LSTM and Convolutional neural network (CNN) are implemented to perform sentiment and behaviour analysis of the tweets. A novel approach of combining Vader and NRC lexicon is used to generate the sentiment and emotion polarity and categories. The evaluation metrics such as accuracy, mean absolute error and mean square error are used to test the performance of the model. The business use cases for the models applied here can be to understand the opinion of customers towards their business to improve their service. Contradictory to the suggestions of Google’s S/W ratio method, LSTM models performed better than using CNN models for categorical as well as regression problems.

A sentiment analysis tool based on Learning Sentiment- Specific Word Embedding for tweets classification

Let us look at how we can implement text classification with Tensorflow https://www.tensorflow.org/ TensorFlow is an end-to-end open source platform for machine learning. The dataset is from the Tweet Sentiment Extraction challenge from Kaggle(https://www.kaggle.com/c/tweet-sentiment-extraction/overview) We would implement text classification using a simple convolutional network developed using Tensorflow on tweet data to classify tweets as "positive","negative" or "neutral"

A Natural Language Processing project that performs sentiment analysis on Twitter data using TF-IDF and Logistic Regression. The model classifies tweets as positive or negative and evaluates performance using accuracy and classification report.

A very simple repo for Text Classification, Sentiment Identification and Headline generator for Mobile tech. articles and tweets.

Sentiment analysis on tweets analyzing any trade-able asset (Cryptocurrency) using Machine Learning and statistical classification model

In this repository you will find a jupyter notebook for implementing a Neural Network based text classification using TensorFlow 2.1. I am specifically training a simple neural network based text classification model to classify sentiment of tweets . I will be using the tensorflow gpu version.

I qt worked on corona virus tweet streams mam With hashtags #covid19,#indiafightscorona,#lockdown I did generate the dastset from the stream and procesed according to the working of deep learning algorithms work flow. I reframed my datset with 2 parameters-- tweets full text and sentiment score and worked on 4 algorithms mam. SET 1- DEEP LEARNING ALGOTITHMS: 1.CNN -(used 1csv with train_test_split method ) Accuracy-0.73368 2.LSTM- (used 2csv file seperate for trainingand testing) Training accuracy-0.9457,loss-0.1605 Testing accuracy-0.6557,loss-0.3442 3.FFNN-( used 2csv file seperate for trainingand testing) Training accuracy-0.28,loss-622.3 Testing accuracy-0.14893,loss-141.82 4.ANN with TFIDF Vectorizer(used 1 csv wth train_test_split) The different Ann epoches and models with different learning rate and different drop out value ,Training accuracy ranged btween 0.4752 to 0.6270 and the Validation accuracy ranged 0.2353 constantly On comparing the above 4 algorithms I came to a conclusiom with my understanding Sentiment analysis in tweets can be done efficiently in this order. CNN > LSTM > ANN > FFNN. SET 2-MACHINE LEARNING I did try with Linear Support vector Classifier --1 csv train_test_split method Training accuracy - 0.6666 Testing accuracy(f1score)-0.59471 And with Naive bayes classifier--1 csv train_test_split method Training accuracy - 0.64 Test accuracy -0.5486 SET 3- MODEL CLASSIFICATIONS: I compared my datasets efficiency with 4 models . The accuracies of the model classificatiom are: 1.Baseline Model - 62.86% 2.Reduces Model-65.71% 3.Regularized Model-66.86% 4.Dropout Model-67.43% Efficient modeling order for tweet data-set Dropout model > Regularized model > Reduced model > Baseline model .

An innovative six-class classification model and web application that is able to predict sentiments of tweets around you.

This project has taken US Airlines Twitter Dataset (Training 15000 tweets & Testing 3000 tweets). It uses machine learning to classify the sentiments of tweets into positive, neutral and negative. It uses Naive-Bayes Classifier for text-classification and NLTK and SkLearn libraries in python.

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Tokenization, WordCloud, vectorization and classification of tweets based on positive, neutral and negative sentiment

Analyzed effect of Emoji's in improving Sentiment Analysis results. Collected twitter data using Twitter StreamAPI and used TF-IDF to vectorize the tweets. Created a positive and negative vector using the matrix, and used cosine similarity to identify the extent to which a given tweet is positive or negative. Incorporated Emoji's to the tweets by converting unicode, and repeated the process. Improved classification of the process by 15%.

Using NLP principles we need to build a model that analysis Arabic tweets that related to COVID-19

Smart Sentiment Analysis and Classification of Arabic Tweets by building and merging deep learning models

Machine Learning project in Text Sentiment Classification: predict if a tweet used to contain a positive :) or negative :( smiley, by considering only the remaining text.

A simple tweet sentiment classification using Naive Bayes Classifier

🐦 Sentiment analysis of tweets based on 6 universal human emotions. Final project for the course "Classification approaches for Social Media Text" at the UP.

This project focuses on scraping airline customer reviews from Twitter and applying NLP-based sentiment analysis using machine learning and deep learning models. It includes tweet extraction, text preprocessing, feature engineering, sentiment classification, and result visualization to evaluate public perception of airline services.

Sentiment analysis deals with identifying and classifying opinions or sentiments expressed in source text. Social media is generating a vast amount of sentiment rich data in the form of tweets, status updates, blog posts etc. Sentiment analysis of this user generated data is very useful in knowing the opinion of the crowd. Twitter sentiment analysis is difficult compared to general sentiment analysis due to the presence of slang words and misspellings. The maximum limit of characters that are allowed in Twitter is 140. Knowledge base approach and Machine learning approach are the two strategies used for analyzing sentiments from the text. In this paper, I try to analyze the twitter posts about electronic products like mobiles, laptops etc using Machine Learning approach. By doing sentiment analysis in a specific domain, it is possible to identify the effect of domain information in sentiment classification. I present a new feature vector for classifying the tweets as positive, negative and extract peoples' opinion about products

Data retrieved from Kaggle.com; it is titled “Political Social Media Posts” and consists of 5000 observations with 21 variables. We analyze this data set based on questions that involve natural language processing of one or more variables. Our ultimate goal is to analyze the sentiment of each tweet and see how it varies by different attributes such as audience, message, and political party. In addition, we did Regression and Classification with the label of controversiality to see if it can predict bias.

Abstract Depression brings significant challenges to the overall global public health. Each day, millions of people suffered from depression and only a small fraction of them undergo proper treatments. In the past, doctors will diagnose a patient via a face to face session using the diagnostic criteria that determine depression such as the Depression DSM-5 Diagnostic Criteria. However, past research revealed that most patients would not seek help from doctors at the early stage of depression which results in a declination in their mental health condition. On the other hand, many people are using social media platforms to share their feelings on a daily basis. Since then, there have been many studies on using social media to predict mental and physical diseases such as studies about cardiac arrest (Bosley et al., 2013), Zika virus (Miller, Banerjee, Muppalla, Romine, & Sheth, 2017), prescription drug abuse (Coppersmith, Dredze, Harman, Hollingshead, & Mitchell, 2015) mental health (De Choudhury, Kiciman, Dredze, Coppersmith, & Kumar, 2016) and studies particularly about depressive behavior within an individual (Kiang, Anthony, Adrian, Sophie, & Siyue, 2015). This research particularly focuses on leveraging social media data for detecting depressive thoughts among social media users. In essence, this research incorporated text analysis that focuses on drawing insights from written communication in order to conclude whether a tweet is related to depressive thoughts. This research produced a web application that performs a real-time enhanced classification of tweets based on a domain-specific lexicon-based method, which utilizes an improved dictionary that consists of depressive and non-depressive words with their associated orientations to classify depressive tweets. Problem understanding or Business Understanding Depression is the main cause of disability worldwide (De Choudhury et al., 2013). Statistically, an estimation of nearly 300 million people around the world suffers from depression. Shen et al (2017) mentioned that approximately 70% of people with early stages of depression would not consult a clinical psychologist. Many people are utilizing social media sites like Facebook and Instagram to disclose their feelings. This research persists the hypothesis that there are similarities between the mental state of an individual and the sentiment of their tweets and investigated the potentiality of social media (like twitter) as a data source for classifying depression among individuals.

The main aim of the project is to analyze the Twitter data describing the covid situation and to build a text classification model which can distinguish the tweets into 5 categories such as Extremely Negative (0), Negative (1), Neutral (2), Positive (3) and Extremely Positive (4). The provided dataset contains tweets with dimension (37041, 2) and numerical labels with dimension (37041,2) of above categories separately. However, the provided tweets need to be cleaned as it contains irrelevant elements such as mentions (@), HTTP links, HTML tags, punctuation marks and URL. Using the regex function, I removed those elements and Stopwords from tweets. Apart from this, to normalize the terms, I implemented Porter Stemmer and used WordNet Lemmatizer to convert the term to its base form. After this, to convert the words into vectors of equal length, I tokenized the tweets and converted it to sequence and then post padded the sequence with zero and kept the length of largest sequence in tweets as maximum length. After Preprocessing the data, the Tweet dataset has dimension of (37041, 286). For Model Selection, I build 3 different models consisting of one Baseline model such as Multinomial Naive Bayes and 2 advanced Recurrent Neural Network models such as GRU Architecture with a single Embedding layer, 1 Bidirectional layer followed by Global Average Pooling 1D and 2 Dense layers & LSTM Architecture with a single Embedding layer followed by 2 Bidirectional layers and 2 Dense layers. In addition to this, I also tried applying Dropout with a 40% dropout rate during training of RNN models and Early Stopping method for preventing overfitting and evaluated that Early Stopping gave better results than Dropout. For evaluation of models, I splitted the dataset into training,testing and validation split with (80,10,10) ratio and calculated F1 macro, AUC Score on test data and using the Confusion Matrix, I calculated the accuracy by dividing the sum of diagonal elements by the sum of all elements. In addition to this, I plotted training vs. validation loss and accuracy graphs to visualize the performance of models. Interestingly, by not implementing the preprocessing techniques like removing stopwords, Porter Stemmer or WordNetLemmatizer and using just basic text cleaning function in the RNN model with LSTM architecture, the accuracy of the model was increased from 73.87% to 77.1% and had AUC score of 0.95.

NLP Classification - Bitcoin Tweet Sentiment

Scrapped tweets using twitter API (for keyword ‘Netflix’) on an AWS EC2 instance, ingested data into S3 via kinesis firehose. Used Spark ML on databricks to build a pipeline for sentiment classification model and Athena & QuickSight to build a dashboard