This dataset has been created through a survey wherein 267 UG and PG students of Manipal Institute of Technology, participated and annotated 1000 images for its sentiment score on a scale of 7. Each image was presented to at least three annotators. After collecting all the annotations, we took the majority vote out of the three scores for each image; that is an image annotation is considered valid only when at least two of three annotators agree on the exact label (out of 7 labels). This dataset uses following sentiment label-map: 1-Depressed 2-Very Sad 3-Sad 4-Neutral 5-Happy 6-Very Happy 7-Excited

Explore our Multimodal Sentiment Dataset, featuring 100 diverse classes of images and corresponding texts with sentiment labels. Ideal for AI-driven sentiment analysis, image classification, and multimodal fusion tasks.

This is an image and text dataset for sentiment analysis.

image captioning

The ColorEmoNet dataset has been constructed using foundational concepts from colour theory to explore the relationship between colours and emotions.

The objective of sentiment analysis for movie reviews is to automatically analyze and categorize the sentiments expressed in reviews, providing insights into audience opinions, emotions, and reactions towards films.

This submission contains trained end-to-end models for the Neural Monkey toolkit for Czech and English, solving four NLP tasks: machine translation, image captioning, sentiment analysis, and summarization. The models are trained on standard datasets and achieve state-of-the-art or near state-of-the-art performance in the tasks. The models are described in the accompanying paper. The same models can also be invoked via the online demo: https://ufal.mff.cuni.cz/grants/lsd In addition to the models presented in the referenced paper (developed and published in 2018), we include models for automatic news summarization for Czech and English developed in 2019. The Czech models were trained using the SumeCzech dataset (https://www.aclweb.org/anthology/L18-1551.pdf), the English models were trained using the CNN-Daily Mail corpus (https://arxiv.org/pdf/1704.04368.pdf) using the standard recurrent sequence-to-sequence architecture. There are several separate ZIP archives here, each containing one model solving one of the tasks for one language. To use a model, you first need to install Neural Monkey: https://github.com/ufal/neuralmonkey To ensure correct functioning of the model, please use the exact version of Neural Monkey specified by the commit hash stored in the 'git_commit' file in the model directory. Each model directory contains a 'run.ini' Neural Monkey configuration file, to be used to run the model. See the Neural Monkey documentation to learn how to do that (you may need to update some paths to correspond to your filesystem organization). The 'experiment.ini' file, which was used to train the model, is also included. Then there are files containing the model itself, files containing the input and output vocabularies, etc.

natural language processing

A lexicon of 751 emoji characters with automatically assigned sentiment. The sentiment is computed from 70,000 tweets, labeled by 83 human annotators in 13 European languages. The Emoji Sentiment Ranking web page at http://kt.ijs.si/data/Emoji_sentiment_ranking/ is automatically generated from the data provided in this repository. The process and analysis of emoji sentiment ranking is described in the paper: P. Kralj Novak, J. Smailović, B. Sluban, I. Mozetič, Sentiment of Emojis, submitted; arXiv preprint, http://arxiv.org/abs/1509.07761, 2015.

Interactive Facial Expression and Emotion Detection (IFEED) is an annotated dataset that can be used to train, validate, and test Deep Learning models for facial expression and emotion recognition. It contains pre-filtered and analysed images of the interactions between the six main characters of the Friends television series, obtained from the video recordings of the Multimodal EmotionLines Dataset (MELD).

The images were obtained by decomposing the videos into multiple frames and extracting the facial expression of the correctly identified characters. A team composed of 14 researchers manually verified and annotated the processed data into several classes: Angry, Sad, Happy, Fearful, Disgusted, Surprised and Neutral.

IFEED can be valuable for the development of intelligent facial expression recognition solutions and emotion detection software, enabling binary or multi-class classification, or even anomaly detection or clustering tasks. The images with ambiguous or very subtle facial expressions can be repurposed for adversarial learning. The dataset can be combined with additional data recordings to create more complete and extensive datasets and improve the generalization of robust deep learning models.

RoMEMESv2 is a dataset of Romanian language memes, collected from public social media platforms. The dataset was manually annotated with:

• associated text in Romanian language;
• image complexity;
• polarity;
• sentiment;
• political content.

In addition, the dataset contains associated metadata and the text part was automatically annotated in the RELATE platform with part-of-speech tags, lemmas, and dependency parsing.

Files and folders in this dataset:

• metadata.tsv - contains metadata and annotations; the first column is the file ID;
• LICENSE - contains licensing information;
• README - is this file;
• images - folder containing image files, following the file naming convention ID.extension, where extension is the original file extension (sometimes this may not correspond with the mime/type of the file, as indicated in metadata.tsv);
• text - folder containing text files, following the file naming convention ID.txt; this is only the message from the meme, without additional text (text from logos, unrelated text, etc.);
• conllup - folder containing automatic text annotations for the files in the "text" folder, created in the RELATE platform, following the file naming convention ID.conllup;
• text_complete - folder with the complete text extracted from the meme (contains additional text which may not be directly related to the meme message);
• conllup_complete - folder containing automatic text annotations for the files in the "text_complete" folder, created in the RELATE platform, following the file naming convention ID.conllup.

A first version of this corpus was released here: RoMEMES https://doi.org/10.5281/zenodo.13120215" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13120215
The current version has more data and the additional text_complete and conllup_complete folders. These are new levels of annotation, which were not available in the initial release. To maintain compatibility with existing code, the rest of the data is in the same format. Currently not all memes have the text_complete annotation. In case a text file is missing in one of the folders, use the text from the other folder.

This dataset is a bilingual (Persian-English) collection of 10,000 entries designed for sentiment analysis, including text and corresponding images. It contains two emotional classes and ensures gender balance in the data. Each entry was annotated by three independent labelers to enhance accuracy. The data was collected from social media platforms, primarily Telegram and Twitter.

This repository was created for my Master's thesis in Computational Intelligence and Internet of Things at the University of Córdoba, Spain. The purpose of this repository is to store the datasets found that were used in some of the studies that served as research material for this Master's thesis. Also, the datasets used in the experimental part of this work are included.

Below are the datasets specified, along with the details of their references, authors, and download sources.

----------- STS-Gold Dataset ----------------

The dataset consists of 2026 tweets. The file consists of 3 columns: id, polarity, and tweet. The three columns denote the unique id, polarity index of the text and the tweet text respectively.

Reference: Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold.

File name: sts_gold_tweet.csv

----------- Amazon Sales Dataset ----------------

This dataset is having the data of 1K+ Amazon Product's Ratings and Reviews as per their details listed on the official website of Amazon. The data was scraped in the month of January 2023 from the Official Website of Amazon.

Owner: Karkavelraja J., Postgraduate student at Puducherry Technological University (Puducherry, Puducherry, India)

Features:

• product_id - Product ID
• product_name - Name of the Product
• category - Category of the Product
• discounted_price - Discounted Price of the Product
• actual_price - Actual Price of the Product
• discount_percentage - Percentage of Discount for the Product
• rating - Rating of the Product
• rating_count - Number of people who voted for the Amazon rating
• about_product - Description about the Product
• user_id - ID of the user who wrote review for the Product
• user_name - Name of the user who wrote review for the Product
• review_id - ID of the user review
• review_title - Short review
• review_content - Long review
• img_link - Image Link of the Product
• product_link - Official Website Link of the Product

License: CC BY-NC-SA 4.0

File name: amazon.csv

----------- Rotten Tomatoes Reviews Dataset ----------------

This rating inference dataset is a sentiment classification dataset, containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. On average, these reviews consist of 21 words. The first 5331 rows contains only negative samples and the last 5331 rows contain only positive samples, thus the data should be shuffled before usage.

This data is collected from https://www.cs.cornell.edu/people/pabo/movie-review-data/ as a txt file and converted into a csv file. The file consists of 2 columns: reviews and labels (1 for fresh (good) and 0 for rotten (bad)).

Reference: Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 115–124, Ann Arbor, Michigan, June 2005. Association for Computational Linguistics

File name: data_rt.csv

----------- Preprocessed Dataset Sentiment Analysis ----------------

Preprocessed amazon product review data of Gen3EcoDot (Alexa) scrapped entirely from amazon.in
Stemmed and lemmatized using nltk.
Sentiment labels are generated using TextBlob polarity scores.

The file consists of 4 columns: index, review (stemmed and lemmatized review using nltk), polarity (score) and division (categorical label generated using polarity score).

DOI: 10.34740/kaggle/dsv/3877817

Citation: @misc{pradeesh arumadi_2022, title={Preprocessed Dataset Sentiment Analysis}, url={https://www.kaggle.com/dsv/3877817}, DOI={10.34740/KAGGLE/DSV/3877817}, publisher={Kaggle}, author={Pradeesh Arumadi}, year={2022} }

This dataset was used in the experimental phase of my research.

File name: EcoPreprocessed.csv

----------- Amazon Earphones Reviews ----------------

This dataset consists of a 9930 Amazon reviews, star ratings, for 10 latest (as of mid-2019) bluetooth earphone devices for learning how to train Machine for sentiment analysis.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 5 columns: ReviewTitle, ReviewBody, ReviewStar, Product and division (manually added - categorical label generated using ReviewStar score)

License: U.S. Government Works

Source: www.amazon.in

File name (original): AllProductReviews.csv (contains 14337 reviews)

File name (edited - used for my research) : AllProductReviews2.csv (contains 9930 reviews)

----------- Amazon Musical Instruments Reviews ----------------

This dataset contains 7137 comments/reviews of different musical instruments coming from Amazon.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 10 columns: reviewerID, asin (ID of the product), reviewerName, helpful (helpfulness rating of the review), reviewText, overall (rating of the product), summary (summary of the review), unixReviewTime (time of the review - unix time), reviewTime (time of the review (raw) and division (manually added - categorical label generated using overall score).

Source: http://jmcauley.ucsd.edu/data/amazon/

File name (original): Musical_instruments_reviews.csv (contains 10261 reviews)

File name (edited - used for my research) : Musical_instruments_reviews2.csv (contains 7137 reviews)

Dataset Description: emodata

The emodata dataset is designed to analyze and predict emotions based on numerical labels and pixel data. It is structured to include information about emotion labels, pixel values, and their usage in training and testing. Below is a detailed description of the dataset:

1. General Information - Purpose: Emotion analysis and prediction based on numerical scales and pixel data. - Total Samples: 49,400 - Emotion Labels: Represented as numerical intervals, each corresponding to a specific emotional intensity or category. - Pixel Data: Images are represented as pixel intensity values. - Data Split: - Training set: 82% of the data - Testing set: 18% of the data

2. Emotion Labels

• The labels are grouped into numerical intervals to categorize emotional intensity or types. Each interval corresponds to the count of samples:
  • 0.00 - 0.30: 6,221 samples
  • 0.90 - 1.20: 6,319 samples
  • 1.80 - 2.10: 6,420 samples
  • 3.00 - 3.30: 8,789 samples
  • 3.90 - 4.20: 7,498 samples
  • 4.80 - 5.10: 7,377 samples
  • 5.70 - 6.00: 6,763 samples
• Statistical Summary:
  • Mean: 3.1
  • Standard Deviation: 1.94
  • Quantiles:
  • Minimum: 0
  • 25%: 1
  • Median: 3
  • 75%: 5
  • Maximum: 6

3. Pixel Data

• Unique Values:
  • Total Unique Values: 34,000
• Most Common Pixel Intensities: Common pixel intensity values for various samples are listed, indicating grayscale or color representation.
• Pixel Usage:
  • Training: 82%
  • Testing: 18%

4. Data Quality

• Valid Samples: 100% (49.4k samples)
• Mismatched Samples: 0%
• Missing Samples: 0%

5. Usage

This dataset is particularly suited for: - Emotion Classification Tasks: Training machine learning models to classify emotions based on numerical and image data. - Deep Learning Tasks: Utilizing pixel intensity data for convolutional neural networks (CNNs) to predict emotional states. - Statistical Analysis: Exploring the distribution of emotional intensities and their relationship with image features.

Potential Applications

• Sentiment Analysis
• Emotion Detection in Images
• Human-Computer Interaction Systems
• AI-based Feedback Systems

This dataset provides a comprehensive structure for emotion analysis through a combination of numerical and image data, making it versatile for both machine learning and deep learning applications.

This data is used in the second experimental evaluation of face smile detection in the paper titled "Smile detection using Hybrid Face Representaion" - O.A.Arigbabu et al. 2015.

Download the main images from LFWcrop website: http://conradsanderson.id.au/lfwcrop/ to select the samples we used for smile and non-smile, as in the list.

Kindly cite:

Arigbabu, Olasimbo Ayodeji, et al. "Smile detection using hybrid face representation." Journal of Ambient Intelligence and Humanized Computing (2016): 1-12.

C. Sanderson, B.C. Lovell. Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference. ICB 2009, LNCS 5558, pp. 199-208, 2009

Huang GB, Mattar M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. University of Massachusetts, Amherst, Technical Report

Introduction

Welcome to the African Facial Expression Image Dataset, curated to support the development of advanced facial expression recognition systems, biometric identification models, KYC verification processes, and a wide range of facial analysis applications. This dataset is ideal for training robust emotion-aware AI solutions.

Facial Expression Data

The dataset includes over 2000 high-quality facial expression images, grouped into participant-wise sets. Each participant contributes:

Diversity & Representation

Image Quality & Capture Conditions

To ensure generalizability and robustness in model training, images were captured under varied real-world conditions:

Metadata

Each participant's image set is accompanied by detailed metadata, enabling precise filtering and training:

This metadata helps in building expression recognition models that are both accurate and inclusive.

Use Cases & Applications

This dataset is ideal for a variety of AI and computer vision applications, including:

Secure & Ethical Collection

Dataset Updates & Customization

To support evolving AI development needs, this dataset is regularly updated and can be tailored to project-specific requirements. Custom options include:

The dataset for this project is characterised by photos of individual human emotion expression and these photos are taken with the help of both digital camera and a mobile phone camera from different angles, posture, background, light exposure, and distances. This task might look and sound very easy but there were some challenges encountered along the process which are reviewed below: 1) People constraint One of the major challenges faced during this project is getting people to participate in the image capturing process as school was on vacation, and other individuals gotten around the environment were not willing to let their images be captured for personal and security reasons even after explaining the notion behind the project which is mainly for academic research purposes. Due to this challenge, we resorted to capturing the images of the researcher and just a few other willing individuals. 2) Time constraint As with all deep learning projects, the more data available the more accuracy and less error the result will produce. At the initial stage of the project, it was agreed to have 10 emotional expression photos each of at least 50 persons and we can increase the number of photos for more accurate results but due to the constraint in time of this project an agreement was later made to just capture the researcher and a few other people that are willing and available. These photos were taken for just two types of human emotion expression that is, “happy” and “sad” faces due to time constraint too. To expand our work further on this project (as future works and recommendations), photos of other facial expression such as anger, contempt, disgust, fright, and surprise can be included if time permits. 3) The approved facial emotions capture. It was agreed to capture as many angles and posture of just two facial emotions for this project with at least 10 images emotional expression per individual, but due to time and people constraints few persons were captured with as many postures as possible for this project which is stated below: Ø Happy faces: 65 images Ø Sad faces: 62 images There are many other types of facial emotions and again to expand our project in the future, we can include all the other types of the facial emotions if time permits, and people are readily available. 4) Expand Further. This project can be improved furthermore with so many abilities, again due to the limitation of time given to this project, these improvements can be implemented later as future works. In simple words, this project is to detect/predict real-time human emotion which involves creating a model that can detect the percentage confidence of any happy or sad facial image. The higher the percentage confidence the more accurate the facial fed into the model. 5) Other Questions Can the model be reproducible? the supposed response to this question should be YES. If and only if the model will be fed with the proper data (images) such as images of other types of emotional expression.

Comparison of Price: USD per 1000 image generations, Lower is better by Model

Context and Methodology:

This dataset was created as part of a sentiment analysis project using enriched Twitter data. The objective was to train and test a machine learning model to automatically classify the sentiment of tweets (e.g., Positive, Negative, Neutral).
The data was generated using tweets that were sentiment-scored with a custom sentiment scorer. A machine learning pipeline was applied, including text preprocessing, feature extraction with CountVectorizer, and prediction with a HistGradientBoostingClassifier.

Technical Details:

The dataset includes five main files:

• test_predictions_full.csv – Predicted sentiment labels for the test set.

• sentiment_model.joblib – Trained machine learning model.

• count_vectorizer.joblib – Text feature extraction model (CountVectorizer).

• model_performance.txt – Evaluation metrics and performance report of the trained model.

• confusion_matrix.png – Visualization of the model’s confusion matrix.

The files follow standard naming conventions based on their purpose.
The .joblib files can be loaded into Python using the joblib and scikit-learn libraries.
The .csv,.txt, and .png files can be opened with any standard text reader, spreadsheet software, or image viewer.
Additional performance documentation is included within the model_performance.txt file.

Additional Details:

• The data was constructed to ensure reproducibility.

• No personal or sensitive information is present.

• It can be reused by researchers, data scientists, and students interested in Natural Language Processing (NLP), machine learning classification, and sentiment analysis tasks.

Introduction

Welcome to the Native American Facial Expression Image Dataset, curated to support the development of advanced facial expression recognition systems, biometric identification models, KYC verification processes, and a wide range of facial analysis applications. This dataset is ideal for training robust emotion-aware AI solutions.

Facial Expression Data

The dataset includes over 1000 high-quality facial expression images, grouped into participant-wise sets. Each participant contributes:

Diversity & Representation

Image Quality & Capture Conditions

To ensure generalizability and robustness in model training, images were captured under varied real-world conditions:

Metadata

Each participant's image set is accompanied by detailed metadata, enabling precise filtering and training:

This metadata helps in building expression recognition models that are both accurate and inclusive.

Use Cases & Applications

This dataset is ideal for a variety of AI and computer vision applications, including:

Secure & Ethical Collection

Dataset Updates & Customization

To support evolving AI development needs, this dataset is regularly updated and can be tailored to project-specific requirements. Custom options include: