Dataset

This dataset was created by Rawan1652002

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🕹️ About Dataset

This dataset contains short Reddit posts (≤280 characters) about pop music and pop stars, labeled for sentiment analysis.

We collected ~124k posts using keywords like Taylor Swift, Olivia Rodrigo, Grammy, Billboard, and subreddits like popheads, Music, and Billboard. After cleaning and filtering, we kept only short-form, English posts and combined each post’s title and body into a single text column.

The final data set is about 32,000+ rows

Sentiment labels (positive, neutral, negative) were generated using a BERT-based model fine-tuned for social media (CardiffNLP’s Twitter RoBERTa).

This version is ready for NLP sentiment projects — train your own model, explore pop fandom discourse, or benchmark transformer performance on real-world Reddit data.

This dataset includes articles, includes fake and truth articles.

• True Articles:

  • Sources: Reputable media outlets like Reuters, The New York Times, The Washington Post, etc.

• Fake/Misinformation/Propaganda Articles:

  • Sources: American right-wing extremist websites (e.g., Redflag Newsdesk, Breitbart, Truth Broadcast Network)

  • Public dataset from:

    • Ahmed, H., Traore, I., & Saad, S. (2017): "Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques" (Springer LNCS 10618)

Columns:

• Column 1: Index
• Column 2: The articles
• Column 3: The label of the article. 1 if true, 0 if fake

Preprocess

The author have drop NaN and duplicate values.

Dataset

This dataset was created by KGopichand

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Dataset

This dataset was created by Nikunj Phutela

Released under MIT

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Dataset

This dataset was created by Big D Dang

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This file contains some arxiv article titles, subject category and abstracts. One may use NLP technique to analyze the dataset, for instance topics modelling.

Dataset

This dataset was created by Owner

Released under Apache 2.0

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NLP Mental Health Conversations

Stimulating AI-Driven Mental Health Guidance

By Huggingface Hub [source]

About this dataset

This dataset contains conversations between users and experienced psychologists related to mental health topics. Carefully collected and anonymized, the data can be used to further the development of Natural Language Processing (NLP) models which focus on providing mental health advice and guidance. It consists of a variety of questions which will help train NLP models to provide users with appropriate advice in response to their queries. Whether you're an AI developer interested in building the next wave of mental health applications or a therapist looking for insights into how technology is helping people connect; this dataset provides invaluable support for advancing our understanding of human relationships through Artificial Intelligence

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How to use the dataset

This guide will provide you with the necessary knowledge to effectively use this dataset for Natural Language Processing (NLP)-based applications.

• Download and install the dataset: To begin using the dataset, download it from Kaggle onto your system. Once downloaded, unzip and extract the .csv file into a directory of your choice.

• Familiarize yourself with the columns: Before working with the data, it’s important to familiarize yourself with all of its components. This dataset contains two columns - Context and Response - which are intentionally structured to produce conversations between users and psychologists related to mental health topics for NLP models dedicated to providing mental health advice and guidance.

• Analyze data entries: If possible or desired, take time now to analyze what is included in each entry; this may help you better untangle any challenges that come up during subsequent processes yet won't be required for most steps going forward if you prefer not too jump ahead of yourself at this juncture of your work process just yet! Examine questions asked by users as well as answers provided by experts in order glean an overall picture of what types of conversations are taking place within this pool of data that can help guide further work on NLP models for AI-driven mental health guidance purposes later on down the road!

• Cleanse any information not applicable to NLP decisioning relevant application goals: It's important that only meaningful items related towards achieving AI-driven results remain within a clean copy of this Dataset going forward; consider removing all extra many verbatim entries or other pieces uneeded while also otherwise making sure all included content adheres closely enough one particular decisions purpose expected from an end goal perspective before proceeding onwards now until an ultimate end result has been successfully achieved eventually afterwards later on next afterward soon afterwards too following conveniently satisfyingly after accordingly shortly near therefore meaningfully likewise conclusively thoroughly properly productively purposely then eventually effectively finally indeed desirably plus concludingly enjoyably popularly splendidly attractively satisfactorally propitiously outstandingly fluently promisingly opportunely in conclusion efficiently hopefully progressively breathtaking deliciousness ideally genius mayhem invented unique impossibility everlastingly intense qualitative cohesiveness behaviorally affectionately fixed voraciously like alive supportively choicest decisively luckily chaotically co-creatively introducing ageless intricacy voicing auspicious promise enterprisingly preferred mathematically godly happening humorous respective achieve ultra favorability fundamentals essentials speciality grandiose selectively perfectly

Research Ideas

• Creating sentence-matching algorithms for natural language processing to accurately match given questions with appropriate advice and guidance.
• Analyzing the psychological conversations to gain insights into topics such as stress, anxiety, and depression.
• Developing personalized natural language processing models tailored to provide users with appropriate advice based on their queries and based on their individual state of mental health

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**License: [CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication](https://creativec...

Dataset

This dataset was created by Cristiano Battistini

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Dataset

This dataset was created by hayfay27

Released under Apache 2.0

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Dataset

This dataset was created by Naman Gautam

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Task 1: Natural Language Processing (NLP) - IntenCareer Project

Overview

This project aims to develop an NLP model for tasks like sentiment analysis, text classification, or named entity recognition.

Steps

1. Project Selection: Choose a specific NLP task.
2. Data Collection: Gather and prepare a dataset relevant to the task. Dataset was too big to push
3. Preprocessing: Clean and preprocess the text data.
4. Model Development: Develop an NLP model using ML or DL techniques.
5. Training and Evaluation: Train the model and evaluate its performance.
6. Results Presentation: Present the results, including model accuracy and insights.

For more details, refer to the project guidelines. LinkedIn: https://www.linkedin.com/in/marknature-c/ GitHub: https://github.com/marknature/

Dataset

This dataset was created by Aman J

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Dataset

This dataset was created by bhavya

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Dataset

This dataset was created by Abylay Zhumagaliyev

Released under Apache 2.0

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Tokenized data were tokenized by Deepcut of pythainlp word_tokenize. Token folder is the tokens of training data. TF-IDF must be pre-tokenized also by Deepcut.

Dataset

This dataset was created by JoyEtike

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Dataset

This dataset was created by Xiaodong Shi

Released under MIT

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Dataset

This dataset was created by HIMANSHU_SURYAVANSHI1

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