https://github.com/tunazislam/bma-fb-ad-climate
공식 코드Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral (Source: Oxford Languages)
Image Source: Deep learning for sentiment analysis: A survey
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging
DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis
Constructing Colloquial Dataset for Persian Sentiment Analysis of Social Microblogs
Sarcasm Detection in Twitter -- Performance Impact while using Data Augmentation: Word Embeddings
Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text
Active learning in annotating micro-blogs dealing with e-reputation
A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection
A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection
Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction
Stance in Replies and Quotes (SRQ): A New Dataset For Learning Stance in Twitter Conversations
Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text
Active learning in annotating micro-blogs dealing with e-reputation
Active learning in annotating micro-blogs dealing with e-reputation
Active learning in annotating micro-blogs dealing with e-reputation
Active learning in annotating micro-blogs dealing with e-reputation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.