A related task to sentiment analysis is the subjectivity analysis with the goal of labeling an opinion as either subjective or objective.
For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance.
Ranked #1 on
Text Classification
on TREC-6
CONVERSATIONAL RESPONSE SELECTION SEMANTIC TEXTUAL SIMILARITY SENTENCE EMBEDDINGS SENTIMENT ANALYSIS SUBJECTIVITY ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING WORD EMBEDDINGS
We present EDA: easy data augmentation techniques for boosting performance on text classification tasks.
SENTIMENT ANALYSIS SUBJECTIVITY ANALYSIS TEXT AUGMENTATION TEXT CLASSIFICATION
We explore the properties of byte-level recurrent language models.
Ranked #4 on
Subjectivity Analysis
on SUBJ
In this study, we explore capsule networks with dynamic routing for text classification.
Ranked #4 on
Sentiment Analysis
on MR
4 MULTI-LABEL TEXT CLASSIFICATION SENTIMENT ANALYSIS SUBJECTIVITY ANALYSIS
Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations.
Ranked #1 on
Named Entity Recognition
on CoNLL 2000
DOCUMENT CLASSIFICATION NAMED ENTITY RECOGNITION SENTIMENT ANALYSIS SUBJECTIVITY ANALYSIS WORD EMBEDDINGS
The postprocessing is empirically validated on a variety of lexical-level intrinsic tasks (word similarity, concept categorization, word analogy) and sentence-level tasks (semantic textural similarity and { text classification}) on multiple datasets and with a variety of representation methods and hyperparameter choices in multiple languages; in each case, the processed representations are consistently better than the original ones.
Ranked #8 on
Sentiment Analysis
on MR
SENTIMENT ANALYSIS SUBJECTIVITY ANALYSIS TEXT CLASSIFICATION
This paper introduces to the scientific community the first opinion-level annotated corpus of sentiment and subjectivity analysis in online videos called Multimodal Opinion-level Sentiment Intensity dataset (MOSI).
In this paper, we propose the Gated Multimodal Embedding LSTM with Temporal Attention (GME-LSTM(A)) model that is composed of 2 modules.
To this aim, an important step to detect fake-news is to have access to a credibility score for a given information source.
Sentiment analysis is one of the fastest growing research areas in computer science, making it challenging to keep track of all the activities in the area.