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141 results for “Sentiment”
A cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis
<p>This data is used for Stock Market Prediction. </p>
Sentiment Analysis of RUU PDP with Naive Bayes, Support Vector Machine, and Random Forest Classification Algorithm
<p>Dataset from the results of data crawling via Twitter which discusses the Rancangan Undang Undang Pelindungan Data Pribadi to be used in the sentiment analysis process. The dataset is divided into several parts according to the process executed on RapidMiner.</p>
Dataset for Twitter Sentiment Analysis on Criminal Data Propagation using Naive Bayes Algorithm
<p>This study presents a dataset tailored for conducting sentiment analysis on Twitter regarding the propagation of criminal data. Leveraging the Naive Bayes algorithm, the dataset aims to facilitate research into public perceptions surrounding the dissemination of criminal data on social media platforms. Through a currated collection of tweets, researchers can explore the nuanced sentiments and attitudes expressed by users in response to this phenomenon.</p>
Brussel mobility Twitter sentiment analysis CSV Dataset
<p>SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU’s transition to carbon neutrality. <br>SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages – including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events.<br>This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU’s journey to a sustainable future.<br>The documents uploaded here are part of WP2 whereby novel, interdisciplinary teams were provided funding to undertake activities to develop a policy recommendation related to EU Green Deal policy. Each of these policy recommendations, and the activities that inform them, will be written-up as a chapter in an edited book collection. Three books will make up this edited collection - one on climate, one on energy and one on mobility. <br>As part of writing a chapter for the SSH CENTRE book on ‘Mobility’, we set out to analyse the sentiment of users on Twitter regarding shared and active mobility modes in Brussels. This involved us collecting tweets between 2017-2022. A tweet was collected if it contained a previously defined mobility keyword (for example: metro) and either the name of a (local) politician, a neighbourhood or municipality, or a (shared) mobility provider. The files attached to this Zenodo webpage is a csv files containing the tweets collected.”. </p>
Dataset: Stocksnips AI-Powered Sentiment US ALL Cap ETF (NEWZ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Relative Sentiment Tactical Allocation ETF (MOOD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
BRAIN Journal-Sentiment Analysis on Embedded Systems Blended Courses-Figure 3. Sentiment analysis on extracted themes
<p>Figure 3 is presenting the sentiment analysis results from the point of view of the themes extracted from the corpus. The same preoccupation for the cost of the course is revealed, but this time the fact that MOOCs are free is appreciated. Students perceive that an integration of MOOCs into blended courses leads to a rapid information of the topics, such a feature receiving a high positive score of +3.46. The detailed explanations in this blended approach received a positive impact from the students with a total score of +2.70, but also the gained knowledge is among the most highly rated corpus themes. </p>
BRAIN Journal-Sentiment Analysis on Embedded Systems Blended Courses-Figure 2. Twitter sentiment analysis results
<p> In order to validate our results, the next step was to extract the sentiment analysis from Tweeter’s tweets (Figure 2) which are based on blending embedded systems-related courses. The obtained polarity is positive, so this results shows not only that students appreciated this in a positive manner, but also that the proposed technique for integrating MOOCs into embedded systems courses is a viable one. </p>
BRAIN Journal-Sentiment Analysis on Embedded Systems Blended Courses-Figure 1. Semantria result
<p>In Figure 1 the Semantria output is presented, having a positive polarity, with a score of 0.218. What is interesting to note here are the keywords extracted from students’ feedback. They noticed the integration of MOOCs in the Embedded Systems course as positive due to the fact that the new information is perceived as easier and the gained knowledge seems to be valuable. Students are affected by too many concepts and also by the idea of paying for the course.</p>
Domain-Independent Reviews' Sentiment Polarity Classification using Shallow Word2Seq Convolutional Neural Network
<p>Reviews and comments are perceptions about specific services or products. They are embedded with hidden sentiments the reviewer has towards certain subjects. Business owners use customer reviews to understand customers’ perceptions about specific services or products. The ability to understand reviews’ sentiment from different domains or areas give decision makers and business owners the opportunity to make critical business decisions which can help them to increase profits of their businesses. Previous studies had focussed on classifying sentiment polarity by using traditional machine learning and deep learning methods. However, these suffered from low model generalization ability, causing the models to perform better only on single domain datasets rather than multiple domain datasets. The problem is the inability of the classification model to learn domain-restricted knowledge from multi-domain datasets. Aiming to improve the accuracy of the cross-domain classification, this paper proposes a method which uses Word2Seq Convolutional Neural Network (CNN) to classify reviews’ sentiment across multiple domain datasets (i.e. digital worker, movie, product, hotel and restaurant reviews). The evaluation showed that the proposed method had achieved the state-of-the-art performance. The high classification performance also promoted the reliability and effectiveness of implementing the Word2Seq CNN to classify reviews’ sentiment across different domains and learn domain restricted knowledge while improving the model generalization ability.</p> <p>The uploaded dataset is a sampled dataset with 5000 observations for both training and testing sets.</p>
Webis Tripad Sentiment Corpus 2013 (Webis-Tripad-13-Sentiment)
<p>The Webis Tripad 2013 Sentiment Corpus is a English text corpus of 2100 hotel reviews for the development and evaluation of approaches to sentiment flow analysis. Each document in this corpus is assigned an overall rating score, some metadata, and two kinds of annotations. First, each statement of a review's text has been classified with respect to its sentiment polarity (positive, negative, objective) by Amazon Mechanical Turk (AMT) workers. Second, hotel aspects mentioned in the texts were tagged by in-house domain experts.</p> <p>To give an example, the sentence "The service was perfect and the rooms were clean." consists of two statements "The service was perfect" and "the rooms were clean", both with positive sentiment classification. The aspect in the first statement is "service" and "rooms" in the second, respectively.</p>
SENTIMENT ANALYSIS OF CUSTOMER FEEDBACK IN THE BANKING SECTOR: A COMPARATIVE STUDY OF MACHINE LEARNING MODELS
<p><span>This study investigates the application of sentiment analysis to customer feedback in the banking sector, utilizing natural language processing (NLP) techniques and machine learning models to classify customer sentiments into positive, neutral, and negative categories. Feedback was sourced from online platforms, including bank websites, social media, and third-party review sites. Data preprocessing steps, such as tokenization, stemming, and feature extraction using TF-IDF, were employed to prepare the text for analysis. Various machine learning algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Naïve Bayes, were implemented and evaluated using metrics such as accuracy, precision, recall, and F1-score. The results show that LSTM outperformed all models with a 91% accuracy, followed closely by SVM at 89%. These findings demonstrate the potential of advanced machine learning techniques in accurately classifying sentiments and provide valuable insights into customer satisfaction and areas for improvement within the banking sector. Future work aims to further optimize models for better classification of neutral feedback and explore more advanced deep learning models, such as BERT.</span></p>
Dataset Public Opinion of UAE and Sentiment Analysis Process
<p>This article explains the UAE public opinion towards Indonesia in the era of President Jokowi's administration. In the digital era, in IR studies, it must be understood that public opinion influences a country's foreign policy, including in the economic field. The research team crawled data from Twitter (X), a data set (raw data), to find out the UAE's public opinion. The raw data was then processed using SVM machine learning to find out the UAE public's positive, negative, and neutral levels regarding Indonesia. After that, the results of public opinion were compared with the UAE's and Indonesia's economic cooperation to determine whether there was a relationship between public opinion and the level of economic cooperation between the two countries. So, the data in this study are the UAE public opinion data collection, which involved tracking and analyzing public sentiment in various UAE-based media outlets and sentiment analysis processes.</p>
Sentiment Analysis and Cross-lingual Word Embeddings for Endangered Languages
<p>A sentiment analyzer and cross-lingual word embeddings for endangered languages (e.g., Erzya, Moksha, Skolt Sami, Komi-Zyrian).</p>
Same Sentiment Classification Train/Dev/Test Pair IDs
<p>This "dataset" only includes the compiled pairings of the Yelp Business Review Dataset. To get access to the actual review texts, please follow the <a href="https://www.yelp.com/dataset">instructions on the Yelp Dataset webpage</a>.</p> <p>The data format is <em>JSONlines</em>.<br> Python Load Example:</p> <pre><code class="language-python">import pandas as pd traindev_df = pd.read_json("df_traindev.jsonl", lines=True) test_df = pd.read_json("df_test.jsonl", lines=True) # example access to single business/review id s1_bid = test_df.iloc[0]["sent1_business_id"] s1_rid = test_df.iloc[0]["sent1_review_id"] s2_bid = test_df.iloc[0]["sent2_business_id"] s2_rid = test_df.iloc[0]["sent2_review_id"] label = test_df.iloc[0]["is_same_side"]</code></pre> <p>See documentation at:</p> <ul> <li><a href="https://www.yelp.com/dataset/documentation/main">Yelp Dataset Schemata</a> (only <em>business.json</em> and <em>review.json</em> were used)</li> <li><a href="https://www.yelp.com/developers/documentation/v3/all_category_list">Yelp Business Category Hierarchy</a> (download the <a href="https://www.yelp.com/developers/documentation/v3/all_category_list/categories.json">json file</a> as <em>all_category_list.json</em>)</li> </ul> <p>For details on how the data was compiled and used in our experiments, please refer to our <a href="https://github.com/webis-de/emnlp21-same-sentiment">code</a> repository. Other derived data splits can be reproduced deterministically by using the same random seed as in our experiments.</p>
Binary Stanford Sentiment Treebank 2 (SST-2)
<p>Binary Stanford Sentiment Treebank (SST2) is a binary version of SST and Movie Review dataset (the neutral class was removed), that is, the data was classified only into positive and negative classes.</p> <p>The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_<k>.pkl: pandas DataFrame with k-cross validation partition</p>
Stanford Sentiment Treebank (SST)
<p>Stanford Sentiment Treebank (SST) is an extension of Movie Review dataset with fine-grained labels ranging between very positive and very negative. The authors extended the MR by adding a more curated human annotation into 5 classes.<br> </p> <p>The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_<k>.pkl: pandas DataFrame with k-cross validation partition</p>
Gender annotations for Multimodal Opinion-level Sentiment Intensity dataset (MOSI)
<p>Annotations of perceived gender (female/male) for all files of the Multimodal Opinion-level Sentiment Intensity dataset (MOSI) [ arXiv:1606.06259]. The annotations were done by a single German and English speaking male annotator.</p>
CryptoSentiment: A large scale sentiment dataset for cryptocurrencies
<p>CryptoSentiment is a dataset, which contains sentiment information about cryptocurrency assets, gathered by various online sources, and analyzed by FinBERT sentiment extractor. More specifically, we provide a publicly available dataset containing fine-grained sentiment analysis data (minute-basis) about cryptocurrency market collected by different online sources. CryptoSentiment dataset includes 235,907 sentiment scores for 14 different cryptocurrencies gathered from various online sources such as news articles and social media.</p>
Towards Generalization of Machine Learning Models: An Arabic Sentiment Analysis Dataset
<p>This data set consists of approximately 1.64 Million Arabic tweets (shared by their IDs) posted from 2009 to 2020, and their corresponding sentiment using a three-point classification system of Positive, Negative and Neutral/Mixed. No specific locations and/or keywords were specified throughout the data collection to obtain variation in the dialects and topics represented within the dataset. It is important to note that any biases in the proposed dataset in relation to the dialects and/or topics discussed were unintentional.</p> <p><strong>Please use the following citation if you use this data in a paper:</strong></p> <blockquote> <p>Abdaljalil, S., Hassanein, S., Mubarak, H., & Abdelali, A. (2023). Towards Generalization of Machine Learning Models: A Case Study of Arabic Sentiment Analysis. <strong>Proceedings of the International AAAI Conference on Web and Social Media, 17(1), 971-980.</strong></p> </blockquote> <p> </p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.