Bigmood Api
Sentiment analysis of twitter data (API)
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Lstm Sentiment Analysis | 844 | 5 years ago | 20 | mit | Jupyter Notebook | |||||
| Sentiment Analysis with LSTMs in Tensorflow | ||||||||||
| Languagecrunch | 269 | 5 years ago | 3 | Python | ||||||
| LanguageCrunch NLP server docker image | ||||||||||
| Bet.aspnetcore | 41 |  | 1 | 1 | 3 years ago | 47 | November 09, 2021 | 11 | mit | C# |
| Large Collection of Extensions and AspNetCore projects for ML.NET models and integration | ||||||||||
| Sentiment Analysis For Chatbots | 40 | 4 years ago | Jupyter Notebook | |||||||
| Jupyter notebooks for "Sentiment analysis for chatbots" training | ||||||||||
| Tweetmap | 39 | 2 years ago | 3 | apache-2.0 | Java | |||||
| A real time Tweet Trend Map and Sentiment Analysis web application with kafka, Angular, Spring Boot, Flink, Elasticsearch, Kibana, Docker and Kubernetes deployed on the cloud | ||||||||||
| Newsfeed Corpus | 18 | 3 years ago | mit | JavaScript | ||||||
| A Dockerized RSS feed fetcher for NLP work, using asyncio | ||||||||||
| Tweets Nlp Elasticsearch | 16 | 5 months ago | 1 | Python | ||||||
| A quick Elasticsearch/Logstash/Kibana (ELK) 7.x environment to quickly ingest realtime filtered tweets, perform Natural Language Processing (NLP), store in Elasticsearch (ES), and visualize in Kibana. | ||||||||||
| Tf Sentiment Docker | 15 | 6 years ago | Python | |||||||
| A docker image for sentiment analysis on tensorflow | ||||||||||
| Litnlp | 12 | a year ago | 16 | September 03, 2020 | apache-2.0 | Python | ||||
| litNlp 是基于 Tensorflow2.0 实现的一个轻量级的深度情感极性分析库，可以实现细粒度的多级别情感极性训练和预测，是搭建情感分析和文本分类模型的快速方案，具体应用如：民宿顾客意见挖掘，见右边的链接。 | ||||||||||
| Mexca | 11 | 4 months ago | 7 | November 21, 2023 | 1 | apache-2.0 | Python | |||
| Multimodal Emotion eXpression Capture Amsterdam. Pipeline for capturing emotion expressions from multiple modalities (video, audio, text) in the wild. | ||||||||||
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