Accuracy: 90.6
Papers with Code · CC BY-SA 4.0
Sentiment Analysis
Sentiment Analysis is the task of classifying the polarity of a given text. For instance, a text-based tweet can be categorized into either "positive", "negative", or "neutral". Given the text and accompanying labels, a model can be trained to predict the correct sentiment.
Sentiment Analysis techniques can be categorized into machine learning approaches, lexicon-based approaches, and even hybrid methods. Some subcategories of research in sentiment analysis include: multimodal sentiment analysis, aspect-based sentiment analysis, fine-grained opinion analysis, language specific sentiment analysis.
More recently, deep learning techniques, such as RoBERTa and T5, are used to train high-performing sentiment classifiers that are evaluated using metrics like F1, recall, and precision. To evaluate sentiment analysis systems, benchmark datasets like SST, GLUE, and IMDB movie reviews are used.
Further readings:
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Dataset기록된 모델 예시MetricPaperCode
데이터셋
91개논문
Accuracy: 93.54
Accuracy: 96.8
Accuracy: 92.5
Accuracy: 92.2
Accuracy: 86.5
Accuracy: 85.1
Accuracy: 93.3
IMDB (Acc): 57.3Yelp 2013 (Acc): 70.3Yelp 2014 (Acc): 71.4
IMDB (Acc): 56.4Yelp 2013 (Acc): 67.8Yelp 2014 (Acc): 69.2
Accuracy: 82.73
Accuracy: 87.45
라이브러리
Papers with Code 리더보드 row에 연결된 구현 저장소입니다.
github.com/4ai/bellm
1 논문
하위 태스크
AS
Aspect-Based Sentiment Analysis (ABSA)
MS
Multimodal Sentiment Analysis
AS
Aspect Sentiment Triplet Extraction
AT
Aspect Term Extraction and Sentiment Classification
FO
Fine-Grained Opinion Analysis
PD
Pcl Detection
TS
Twitter Sentiment Analysis
AS
Arabic Sentiment Analysis
PS
Persian Sentiment Analysis
VA
Vietnamese Aspect-Based Sentiment Analysis
VS
Vietnamese Sentiment Analysis
TO
target-oriented opinion words extraction