GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → haiker2011 → Awesome Nlp Sentiment Analysis

haiker2011 / Awesome Nlp Sentiment Analysis

Licence: gpl-3.0
📖 收集NLP领域相关的数据集、论文、开源实现，尤其是情感分析、情绪原因识别、评价对象和评价词抽取方面。

Labels

deep-learningmachine-learningnlpsentiment-analysis

Projects that are alternatives of or similar to Awesome Nlp Sentiment Analysis

ai challenger 2018 sentiment analysis
Fine-grained Sentiment Analysis of User Reviews --- AI CHALLENGER 2018
Stars: ✭ 16 (-95.48%)
Mutual labels:  sentiment-analysis
Bert For Rrc Absa
code for our NAACL 2019 paper: "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis"
Stars: ✭ 286 (-19.21%)
Mutual labels:  sentiment-analysis
Aspect Based Sentiment Analysis
A paper list for aspect based sentiment analysis.
Stars: ✭ 311 (-12.15%)
Mutual labels:  sentiment-analysis
Weibo terminator workflow
Update Version of weibo_terminator, This is Workflow Version aim at Get Job Done!
Stars: ✭ 259 (-26.84%)
Mutual labels:  sentiment-analysis
Languagecrunch
LanguageCrunch NLP server docker image
Stars: ✭ 281 (-20.62%)
Mutual labels:  sentiment-analysis
Linusrants
Dataset of Linus Torvalds' rants classified by negativity using sentiment analysis
Stars: ✭ 291 (-17.8%)
Mutual labels:  sentiment-analysis
neuralnets-semantics
Word semantics Deep Learning with Vanilla Python, Keras, Theano, TensorFlow, PyTorch
Stars: ✭ 15 (-95.76%)
Mutual labels:  sentiment-analysis
Personality Detection
Implementation of a hierarchical CNN based model to detect Big Five personality traits
Stars: ✭ 338 (-4.52%)
Mutual labels:  sentiment-analysis
Bertweet
BERTweet: A pre-trained language model for English Tweets (EMNLP-2020)
Stars: ✭ 282 (-20.34%)
Mutual labels:  sentiment-analysis
Awesome Sentiment Analysis
Reading list for Awesome Sentiment Analysis papers
Stars: ✭ 306 (-13.56%)
Mutual labels:  sentiment-analysis
Customer satisfaction analysis
基于在线民宿 UGC 数据的意见挖掘项目，包含数据挖掘和NLP 相关的处理，负责数据采集、主题抽取、情感分析等任务。目的是克服用户打分和评论不一致，实时对在线民宿的满意度评测，包含在线评论采集和情感可视化分析。搭建了百度地图POI查询入口，可以进行自动化的批量查询 POI 信息的功能；构建了基于在线民宿语料的 LDA 自动主题聚类模型，利用主题中心词能找出对应的主题属性字典；以用户打分作为标注，然后 litNlp 自带的字符级 TextCNN 进行情感分析，将情感分类概率分布作为情感趋势，最后通过 POI 热力图的方式对不同地域的民宿满意度进行展示。软件版本请见链接。
Stars: ✭ 262 (-25.99%)
Mutual labels:  sentiment-analysis
Introduction Datascience Python Book
Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications
Stars: ✭ 275 (-22.32%)
Mutual labels:  sentiment-analysis
Reactionrnn
Python module + R package to predict the reactions to a given text using a pretrained recurrent neural network.
Stars: ✭ 299 (-15.54%)
Mutual labels:  sentiment-analysis
Stock Analysis
Regression, Scrapers, and Visualization
Stars: ✭ 255 (-27.97%)
Mutual labels:  sentiment-analysis
Sentimentr
Dictionary based sentiment analysis that considers valence shifters
Stars: ✭ 325 (-8.19%)
Mutual labels:  sentiment-analysis
laravel-nlp
Laravel wrapper for common NLP tasks
Stars: ✭ 41 (-88.42%)
Mutual labels:  sentiment-analysis
Cs291k
🎭 Sentiment Analysis of Twitter data using combined CNN and LSTM Neural Network models
Stars: ✭ 287 (-18.93%)
Mutual labels:  sentiment-analysis
Thesemicolon
This repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.
Stars: ✭ 345 (-2.54%)
Mutual labels:  sentiment-analysis
Troll
Language sentiment analysis and neural networks... for trolls.
Stars: ✭ 330 (-6.78%)
Mutual labels:  sentiment-analysis
Informers
State-of-the-art natural language processing for Ruby
Stars: ✭ 306 (-13.56%)
Mutual labels:  sentiment-analysis
View All Similar Projects ➔

awesome-nlp-sentiment-analysis

收集NLP领域相关的数据集、论文、开源实现，尤其是情感分析、情绪原因识别、评价对象和评价词抽取等方面。

📖 Papers & 🙊 Codes

情感分析

  1. Deep Learning for Sentiment analysis: A Survey. paper

情绪归因

  1. An Ensemble Approach for Emotion Cause Detection with Event Extraction and Multi-Kernel SVMs. paper

  2. Event-Driven Emotion Cause Extraction with Corpus Construction. paper

  3. A Question Answering Approach to Emotion Cause Extraction. paper

  4. A Bootstrap Method for Automatic Rule Acquisition on Emotion Cause Extraction. paper

  5. Emotion Cause Detection for Chinese Micro-Blogs Based on ECOCC Model. paper

  6. Emotion Cause Detection with Linguistic Constructions. paper

  7. Emotion Cause Extraction, A Challenging Task with Corpus Construction. paper

  8. Extracting Causes of Emotions from Text. paper

  9. 基于E-CNN神经网络的情绪原因识别方法. paper

  10. 基于序列标注模型的情绪原因识别方法. paper

  11. 基于文本的情绪自动归因方法研究. paper

  12. A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness. paper

  13. Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts. paper code

评价对象和评价词抽取

  1. Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings. paper code

  2. Character-based BiLSTM-CRF Incorporating POS and Dictionaries for Chinese Opinion Target Extraction. paper code

  3. A Unified Model for Opinion Target Extraction and Target Sentiment Prediction. paper code

  4. 评价对象抽取研究综述. paper

  5. 使用深度长短时记忆模型对于评价词和评价对象的联合抽取. paper

  6. 基于语义和句法依存特征的评论对象抽取研究. paper

  7. 基于条件随机场的评价对象缺省项识别. paper

  8. 基于CRFs和领域本体的中文微博评价对象抽取研究. paper

  9. 基于微博的情感倾向性分析方法研究. paper

  10. 基于迭代两步CRF模型的评价对象与极性抽取研究. paper

  11. 基于句法特征的评价对象抽取方法研究. paper

  12. 基于层叠CRFs的中文句子评价对象抽取. paper

  13. 评价对象及其倾向性的抽取和判别. paper

  14. 基于非完备信息系统的评价对象情感聚类. paper

  15. 基于CRFs的评价对象抽取特征研究. paper

  16. 基于核心句及句法关系的评价对象抽取. paper

  17. 面向特定领域的产品评价对象自动识别研究. paper

  18. 评价对象抽取及其倾向性分析. paper

  19. Aspect extraction for opinion mining with a deep convolutional neural network. paper

  20. Recursive neural conditional random fields for aspect-based sentiment analysis. paper

  21. Coupled multi-layer attentions for co-extraction of aspect and opinion terms. paper

💾 Dataset

评价对象和评价词抽取

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].