natural-language-processing-8

Subjectivity Analysis

3260 papers • 126 benchmarks • 313 datasets

A related task to sentiment analysis is the subjectivity analysis with the goal of labeling an opinion as either subjective or objective.

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SUBJ

Czech Subjectivity Dataset

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SUBJ

Czech Subjectivity Dataset

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Most implemented papers

Universal Sentence Encoder

Daniel Matthew Cer, Yinfei Yang, C. Tar, Noah Constant, Nan Hua, Yun-Hsuan Sung, Sheng-yi Kong, Nicole Limtiaco, Rhomni St. John, Mario Guajardo-Cespedes, Steve Yuan, B. Strope, R. Kurzweil•Wed Mar 28 2018

It is found that transfer learning using sentence embeddings tends to outperform word level transfer with surprisingly good performance with minimal amounts of supervised training data for a transfer task.

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EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Jason Wei, Kai Zou•Wed Jan 30 2019

EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion, which shows that EDA improves performance for both convolutional and recurrent neural networks.

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MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos

Amir Zadeh, Louis-philippe Morency, Rowan Zellers, Eli Pincus•Sun Jun 19 2016

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), which is rigorously annotated with labels for subjectivity, sentiment intensity, per-frame and per-opinion annotated visual features, andper-milliseconds annotated audio features.

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All-but-the-Top: Simple and Effective Postprocessing for Word Representations

P. Viswanath, Jiaqi Mu, S. Bhat•Sat Feb 04 2017

This paper demonstrates a counter-intuitive, postprocessing technique -- eliminate the common mean vector and a few top dominating directions from the word vectors -- that renders off-the-shelf representations even stronger.

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Investigating Capsule Networks with Dynamic Routing for Text Classification

Zhou Zhao, Jianbo Ye, Wei Zhao, Min Yang, Zeyang Lei, Suofei Zhang•Wed Mar 28 2018

This work proposes three strategies to stabilize the dynamic routing process to alleviate the disturbance of some noise capsules which may contain “background” information or have not been successfully trained.

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The evolution of sentiment analysis - A review of research topics, venues, and top cited papers

M. Mäntylä, D. Graziotin, Miikka Kuutila•Sun Dec 04 2016

A computer-assisted literature review, where the roots of sentiment analysis are in the studies on public opinion analysis at the beginning of 20th century and in the text subjectivity analysis performed by the computational linguistics community in 1990's, and the top-20 cited papers from Google Scholar and Scopus are presented.

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Learning to Generate Reviews and Discovering Sentiment

I. Sutskever, Alec Radford, R. Józefowicz•Tue Apr 04 2017

The properties of byte-level recurrent language models are explored and a single unit which performs sentiment analysis is found which achieves state of the art on the binary subset of the Stanford Sentiment Treebank.

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Multimodal sentiment analysis with word-level fusion and reinforcement learning

Paul Pu Liang, Amir Zadeh, Louis-philippe Morency, T. Baltrušaitis, Minghai Chen, Sen Wang•Thu Nov 02 2017

The Gated Multimodal Embedding LSTM with Temporal Attention model is proposed that is composed of 2 modules and able to perform modality fusion at the word level and is able to better model the multimodal structure of speech through time and perform better sentiment comprehension.

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Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms

Chunyuan Li, Ricardo Henao, L. Carin, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Guoyin Wang, Dinghan Shen, Wenlin Wang•Wed May 23 2018

This paper conducts a point-by-point comparative study between Simple Word-Embedding-based Models (SWEMs), consisting of parameter-free pooling operations, relative to word-embedding-based RNN/CNN models, and proposes two additional pooling strategies over learned word embeddings: a max-pooling operation for improved interpretability and a hierarchical pooling operation, which preserves spatial information within text sequences.

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Entailment as Few-Shot Learner

Hanzi Mao, Han Fang, Madian Khabsa, Sinong Wang, Hao Ma•Wed Apr 28 2021

A new approach to reformulate potential NLP task into an entailment one, and then fine-tune the model with as little as 8 examples, which improves the various existing SOTA few-shot learning methods by 12\%, and yields competitive few- shot performance with 500 times larger models, such as GPT-3.

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